SHORT BIOGRAPHY

Eyke H ¨ullermeier Department of Mathematics and Computer Science University of Marburg [email protected] http://www.uni-marburg.de/fb12/kebi

Eyke Hullermeier,¨ born in 1969, holds M.Sc. degrees in mathematics and business computing, both from the University of Paderborn (Germany). From the Computer Science Department of the same university he obtained his Ph.D. in 1997 and a Habilitation degree in 2002. From 1998 to 2000, he spent two years as a Marie Curie fellow at the Institut de Recherche en Informatique de Toulouse (France), and held appointments at the Universities of Dortmund, Marburg, and Magde- burg afterwards. He joined the Department of Mathematics and Computer Science at Marburg University (Germany) as a full professor in 2007. His research interests are focused on methodological foundations of Artificial Intelligence and in- telligent systems design, especially on machine learning and data mining as well as modeling and reasoning with uncertain knowledge. Besides, he is very interested in the application of AI methods in other fields, including the life sciences, engineering and economics. He has pub- lished around 200 research papers on these topics in top-tier journals and major international conferences, and several of his contributions have been recognized with scientific awards. Professor Hullermeier¨ is Co-Editor-in-Chief of Fuzzy Sets and Systems (ranked second most influential journal in applied mathematics, based on the number of citations) and serves on the editorial board of various other journals, including Machine Learning, IEEE Transactions on Fuzzy Systems and International Journal of Approximate Reasoning. Moreover, he is a regular member of the program committee of major international conferences in the fields of Computational and Artificial Intelligence (recently, for example, he was area chair at AAAI-2012, ECML-2012, and ICML–2010). He is a coordinator of the EUSFLAT working group on Machine Learning and Data Mining and the head of the IEEE CIS Task Force on Machine Learning. Professor Hullermeier¨ was the General Chair of IPMU–2010, 13th International Conference on In- formation Processing and Management of Uncertainty in Knowledge-Based Systems (Dortmund, Germany). In 2012, he was a PC Co-Chair of SUM, the 6th International Conference on Scalable Uncertainty Management (Marburg, Germany), and last year, he was a PC Co-Chair of DS–2013, the 16th International Conference on Discovery Science (held in Singapore). This year, he is PC Co-Chair of ECML/PDKK, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. SCIENTIFIC VITA 02/2014

Eyke H ¨ullermeier Cilly-Schafer-Straße¨ 7 D–35037 Marburg [email protected] http://www.uni-marburg.de/fb12/kebi

PERSONAL DATA

Date of Birth: February 11, 1969 Place of Birth: Lubbecke¨ (Germany) Nationality: German Marital Status: married since September 15, 1998, two children Academic Degrees: Dr. rer. nat. (doctor of the natural sciences) Diplom-Mathematiker (MSc Mathematics) Diplom-Wirtschaftsinformatiker (MSc Business Informatics)

EDUCATION

Habilitation in Computer Science (January 2002) Department of Mathematics and Computer Science, University of Paderborn, Germany Title of thesis: ‘Similarity-Based Inference: Models and Applications’ Reviewers: Prof. Dr. Kleine Buning,¨ Prof. Dr. Suhl (University of Paderborn), Dr. Prade (IRIT, Toulouse), Prof. Dr. Bouchon-Meunier (Universite´ Pierre et Marie Curie, Paris)

PhD in Computer Science (April 1994 – January 1997) Department of Mathematics and Computer Science, University of Paderborn, Germany Title of thesis: ‘Reasoning about Systems based on Incomplete and Uncertain Models’ Grade: – mit Auszeichnung – (summa cum laude) Reviewers: Prof. Dr. Kleine Buning¨ (University of Paderborn), Prof. Dr. Cellier (University of Arizona, USA), Prof. Dr. Ruckert¨ (Heinz Nixdorf Institut, Paderborn)

MSc Mathematics (October 1991 – December 1996) Department of Mathematics and Computer Science, University of Paderborn, Germany Major areas of study: numerical mathematics, stochastics, differential equations, mathema- tical methods of operations research, minor: economics. Grade: – mit Auszeichnung – (1,0)

MSc Business Informatics (October 1989 – December 1993) Department of Economics, University of Paderborn, Germany Major areas of study: knowledge-based systems, information systems, statistics, econome- trics and economic theory, operations research and production systems. Grade: – sehr gut – (1,0)

I/XVI EXPERIENCE

January 2007 – present Full Professor (W3), Practical Computer Science Department of Mathematics and Computer Science, University of Marburg

December 2004 – December 2006 Associate Professor (C3), Data and Knowledge Engineering Faculty of Computer Science, University of Magdeburg

September 2004 – November 2004 Associate Professor (C3 in substitution), Computational Intelligence Department of Computer Science, University of Dortmund

July 2002 – August 2004 Juniorprofessor (C2), Intelligent Systems Department of Mathematics and Computer Science, University of Marburg

March 2002 – June 2002 Assistant Professor (Hochschuldozent, C2) Department of Mathematics and Computer Science, University of Paderborn

August 2001 – February 2002 Assistant Professor (Wissenschaftlicher Assistent, C1) Automata and Circuit Theory (Prof. Dr. Reusch) Department of Computer Science, University of Dortmund

April 1998 – June 2001 Assistant Professor (Wissenschaftlicher Assistent, C1) Statistics, Econometrics, and Decision Theory (Prof. Dr. Skala) Department of Economics, University of Paderborn

April 1997 – March 1998 Graduate Research Assistant (Wissenschaftlicher Mitarbeiter) Knowledge-Based Systems (Prof. Dr. Kleine Buning)¨ Department of Mathematics and Computer Science, University of Paderborn

February 1997 – March 1997 Visiting Scientist Economic Theory (Prof. Dr. Weise) Department of Economics, University of Kassel, Germany

April 1994 – January 1997 PhD Student Graduate Center at the Heinz Nixdorf Institut, Interdisciplinary Research Center for Com- puter Science and Technology within the University of Paderborn

March 1992 – March 1994 Undergraduate Research Assistant Statistics, Econometrics, and Decision Theory (Prof. Dr. Skala) Department of Economics, University of Paderborn

II/XVI RESEARCH VISITS AND TEMPORARY EMPLOYMENTS ABROAD

• January 2014, Invited Professor at LAMSADE, Universite´ Paris Dauphine

• December 2008, Invited Professor at LIP6, Universite´ Pierre et Marie Curie, Paris

• July 8-22, 2007, Visiting Scientist at the LIRMM – Laboratoire d’Informatique, de Robo- tique et de Microelectronique´ de Montpellier, Universite´ Montpellier

• March/April 2004, Invited Professor at the IRIT – Institut de Recherche en Informatique de Toulouse, Universite´ Paul Sabatier

• November 1998 – October 2000, Visiting Scientist (post-doc) in the Artificial Intelligence and Cognitive Systems Group at the IRIT – Institut de Recherche en Informatique de Toulouse, Universite´ Paul Sabatier

CALLS TO PROFESSORSHIPS

• Computer Science (W3), Faculty of Computer Science, Electrical Engineering and Mathe- matics, University of Paderborn, May 2014.

• Practical Computer Science (W3), Department of Mathematics and Computer Science, Uni- versity of Marburg, August 2006

• Data and Knowledge Engineering (C3), Faculty of Computer Science, University of Magde- burg, September 2004

• Computational Intelligence (C3), Department of Computer Science, University of Dortmund, August 2004

• Intelligent Systems (Juniorprofessorship, C2), Department of Mathematics and Computer Science, University of Marburg, February 2002

Shortlisted:

• Machine Learning (W3), Faculty of Electrical Engineering and Computer Science, TU Berlin, 2006

• Bioinformatics (W3), Faculty of Mathematics and Computer Science, University of Jena, 2006

• Knowledge-Based Systems (C3), Department of Electrical Engineering and Computer Sci- ence, University of Siegen, 2004

AWARDS, HONORS, SCHOLARSHIPS

• Best Paper Award at ICCBR–2013, 21st International Conference on Case-Based Reason- ing, Saratoga Springs, NY, USA.

• Best Paper Award at ECAI–2012, 20th European Conference on Artificial Intelligence, Mont- pellier, France.

• Outstanding Area Chair Award at AAAI–2012, 26th Conference on Artificial Intelligence, Toronto, Canada.

• Best Paper Award at ICCBR–2011, 19th International Conference on Case-Based Reason- ing, London, UK.

III/XVI • Student Best Paper Award (senior author) at ECML/PKDD–2009, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bled, Slovenia.

• Best Paper Award at ECCBR–2008, 9th European Conference on Case-Based Reasoning, Trier, Germany.

• IEEE Transactions on Fuzzy Systems Outstanding Paper Award 2002 (awarded in 2005).

• Runner up paper award at ECAI-2002, 15th European Conference on Artificial Intelligence, Lyon, France.

• Annual prize for outstanding PhD dissertation, awarded by the University of Paderborn (Forderpreis¨ der Universitatsgesellschaft¨ Paderborn, 1997).

• Student Best Paper Award at IFSA–2005, International Fuzzy Systems Association World Congress (senior author), Beijing, China.

• Distinguished Paper Award at EUSFLAT–2007, 5th Conference of the European Society for Fuzzy Logic and Technology, Ostrava, Czech Republic.

• Nominated for the “Heinz Maier-Leibnitz Preis” (German national price for young researchers) independently by the University of Marburg (2004) and the University of Paderborn (2000).

• Marie Curie Research Grant, funded by the European Commission (Programme: Training and Mobility of Researchers (TMR); Title of project: “Case-based decision making: Theory and applications”), November 1998–October 2000.

• PhD scholarship of the Deutsche Forschungsgemeinschaft (DFG, German Research Foun- dation) and the Stiftung Westfalen (Westphalia-Foundation), April 1994 – January 1997.

• Best Paper Award at CASYS–2000, 4th International Conference on Computing Anticipa- tory Systems, Liege,` Belgium.

TEACHING

• Lecturer for courses on logic, discrete mathematics, efficient algorithms, bioinformatics, machine learning and data mining, intelligent systems, knowledge processing, fuzzy sets, databases, statistics, decision theory, operations research (graduate and undergraduate level).

• Invited professor in the Master Program “Soft Computing and Intelligent Systems”, University of Granada, Spain, March 2009.

• Two-day guest lectures on “Fuzzy Data Mining”, University of Aalborg at Esbjerg, Denmark, October 2008 and November 2011.

• Invited one-day lecture on “How Similar is Similar? Methods for Sequence Analysis”, part of the workshop ‘Bioinformatics’ at Max-Planck-Institute for Terrestrial Microbiology, Marburg (Germany), February 2004 and February 2007.

Results of the evaluation of lectures (as done occasionally by the students council, grades on a scale from 1 (best) to 6 (worst)):

IV/XVI Lecture Semester Grade Lecturer Grade Lecture Bioinformatik WS 06/07 1,78 1,78 Fuzzy-Systeme SS 07 1,70 1,90 Bioinformatik WS 07/08 1,33 1,20 Logik und Diskrete Mathematik WS 07/08 1,90 2,45 Fuzzy-Systeme SS 08 2,00 2,22 Maschinelles Lernen SS 08 1,83 2,17 Computational Intelligence SS 09 1,75 1,75 Logik und Diskrete Mathematik WS 09/10 1,78 2,11 Effiziente Algorithmen WS 10/11 1,70 2,00

RESEARCH PROJECTS

• Reinforcement Learning with Qualitative Feedback. Joint project with Prof. J. Furnkranz,¨ Technical University Darmstadt. Financing: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), part of Priority Programme ‘Autonomous Learning’, since 06/2012.

• Praferenzbasiertes¨ CBR: Modellieren, Lernen und Verarbeiten von Erfahrungswissen im Case-Based Reasoning auf der Grundlage praferenzbasierter¨ Methoden. Financing: Deut- sche Forschungsgemeinschaft (DFG, German Research Foundation), since 11/2010.

• Data-Driven Design of Evolving Fuzzy Systems: Enhancing Interpretability, Reliability, and User-Interaction. Joint project with Prof. P. Klement, University of Linz, Austria. Financ- ing: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), since 08/2010.

• Computational Methods in Structural Bioinformatics and Comparative Genomics. Part of the Research Center for Synthetic Microbiology, Marburg. Financing: Ministry for Science and Art, State of Hesse, 01/2010–01/2014.

• Learning by Pairwise Comparison for Problems with Structured Output Spaces. Joint project with Prof. J. Furnkranz,¨ Technical University Darmstadt. Financing: Deutsche Forschungs- gemeinschaft (DFG, German Research Foundation), 12/2007–04/2013.

• Learning of Fuzzy Preference Models: Methods and Applications in Personalized Informa- tion Systems. Financing: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), 04/2005–10/2011.

• Kreuzkorrelation von Proteinbindetaschen zum Erkennen verwandter Bindungsepitope, uner- warteter Nebenwirkungsprofile und funktioneller Verwandtschaften. Joint project with the Institute of Pharmaceutical Chemistry, University of Marburg (Prof. Klebe). Financing: Deut- sche Forschungsgemeinschaft (DFG, German Research Foundation), 12/2004–12/2013.

• Intelligent Methods for Product Evaluation in Automated Quality Control. Financing: Bun- desministerium f ¨urWirtschaft und Technologie (BMWi, German Ministry for Economy and Technology), 03/2006–03/2009.

• Research Cooperation with Siemens Corporate Research, Princeton, NJ, USA, 2005.

• Intelligent Methods for Product Evaluation in Automated Quality Control. Financing: Bat- tenberg Robotics, Marburg, 03/2004–02/2005.

• Statistical Analysis of the Effectiveness of Alternative Therapies for Muscular Diseases. Fi- nancing: Weserbergland-Klinik Hoxter¨ , 04/1997–03/1999.

V/XVI INVITED PRESENTATIONS

Plenary/Keynote Speaker at Conferences

• ‘Learning from Imprecise and Fuzzy Data: On the Notion of Data Disambiguation’. Interna- tional Joint Conference on Computational Intelligence, IJCCI–2013, Faro, Portugal, 2013.

• ‘Preference Learning using Statistical Models for Label Ranking’. European Conference on Data Analysis, ECDA–2013, Luxembourg, 2013.

• ‘Fuzzy Systems Modeling: Development, Critique, and New Directions’. International Confe- rence on Control, Decision and Information Technologies, CoDIT–2013, Hammamet, Tunisia, 2013.

• ‘Fuzzy Logic in Machine Learning’. International Symposium on Computational Intelligence and Design, ISCID–2012, Hangzhou, China, 2012.

• ‘Fuzzy Systems Modeling: From Knowledge-driven to Data-driven Approaches’. 19th East West Zittau Fuzzy Colloquium, Zittau, Germany, 2012.

• ‘Preference Learning’ (jointly with J. Furnkranz).¨ 14th International Conference on Discovery Science, Espoo, Finland, 2011.

• ‘Fuzzy Logic in Machine Learning’, Brazilian Symposium on Intelligent Automation, Sao Joao del-Rei, Brazil, 2011.

• ‘Fuzzy Logic in Machine Learning and Data Analysis’, 4th International Conference on Fuzzy Information and Engineering, ICFIE–2010, Amol, Iran, October 2010.

• ‘Fuzzy Pattern Trees for Quality Assessment: Conception and Model Construction’, Interna- tional Conference on Integrated Systems, Design and Technology, Siegen, Germany, June 2010.

• ‘Fuzzy Rule Induction and Related Problems: A Case for Genetic Search?’, Fourth Interna- tional Workshop on Genetic and Evolutionary Fuzzy Systems, GEFS–2010, Mieres, Spain, March 2010.

• ‘Preference Learning, Ranking, and Similarity Retrieval’, Eighth International Conference on Flexible Query Answering Systems, Roskilde, Denmark, October 2009.

• ‘Learning Valued Preference Structures: Toward an Alternative Decision-Theoretic Frame- work for Machine Learning’, Lernen–Wissen–Adaption, Darmstadt, Germany, September 2009.

• ‘Preference Learning’, EUROFUSE Workshop Preference Modelling and Decision Analysis, Pamplona, Spain, September 2009.

• ‘Fuzzy Logic in Machine Learning’, World Congress of the International Fuzzy Systems As- sociation, Lisbon, July 2009.

• ‘Fuzzy Logic in Machine Learning’. 30th Linz Seminar on Fuzzy Set Theory, Linz (Austria), February 2009.

• ‘Credible Case-Based Inference Using Similarity Profiles’. International Conference on Case- Based Reasoning, Belfast (Northern Ireland), August 2007.

• ‘Learning by Pairwise Comparison: Classification, Ranking, and Related Problems’. Dutch- Belgian Conference on Machine Learning, Ghent (Belgium), May 2006.

VI/XVI • ‘Fuzzy Sets in Data Mining: Useful or Nor?’. French Conference on Fuzzy Logic and Its Applications, Toulouse (France), October 2006.

• ‘Fuzzy Methods in Knowledge Discovery’. 8th International Conference on Computational Intelligence, Dortmund (Germany), October 2004.

Invited Tutorials

• ‘Preference Learning’, ADT–2011, 2nd International Conference on Algorithmic Decision Theory, Rutgers University, New Jersey, USA.

• ‘Preference Learning’ (jointly with J. Furnkranz),¨ ECAI–2012, 20th European Conference on Artificial Intelligence, Montpellier, France.

Workshops

• ‘Preference Learning: An Introduction’. Workshop DA2PL: From Multiple Criteria Decision Aid to Preference Learning, Mons, Belgium, November 15–16, 2012.

• ‘Learning from Imprecise Data: On the Notion of Data Disambiguation’. Workshop on Har- nessing the Information Contained in Low-Quality Data, Oviedo/Mieres, Spain, May 16–17, 2012.

• ‘Bipartite Ranking through Minimization of Univariate Loss’. SIAM Conference on Optimiza- tion, Minisymposium on Algorithms for Ranking, Darmstadt, Germany, May 18, 2011.

• ‘Multi-Label Classification: Challenges, Pitfalls and Perspectives’. MLD–2010, 2nd Interna- tional Workshop ‘Learning from Multi-Label Data’, Haifa, Israel, June 26, 2010.

• ‘Uncertainty, Fuzziness and Ignorance in Knowledge Representation and Learning’. Work- shop on Uncertainty Handling, University of Ulm, Blaubeuren, June 14–16, 2010.

• ‘Graph-Based Modeling and Algorithms in Structural Bioinformatics’, Workshop Algorithmen und Komplexitat,¨ GI Jahrestagung 2009 (Conference of the German Informatics Society), Lubeck,¨ Germany, October 2009.

• ‘Applications of Fuzzy Logic in Machine Learning and Bioinformatics’. International Work- shop ‘Soft Computing: Where Theory meets Applications’, Ostrava, Czech Republic, De- cember 13–16, 2009.

• ‘Fuzzy Logic in Machine Learning’. Rumanian-German Symposium on Mathematics and Its Applications, Sibiu (Romania), May 2009.

• ‘Preference Learning’. 8th International Invitational Workshop ‘Similarities and Preferences’, Udine (Italy), October 2006.

• ‘Decision under Uncertainty: A Case-Based Approach Using Concepts from Fuzzy Set The- ory’. Berlin-Brandenburgische Akademie der Wissenschaften, Berlin, May 2005.

• ‘A Systematic Approach to the Assessment of Fuzzy Association Rules’. Workshop on Al- ternative Techniques for Data Mining and Knowledge Discovery, Brighton, UK, November 2004.

• ‘On Searching Vague Patterns in Data’. GMA-GI-Workshop Fuzzy Systeme, Dortmund (Ger- many), November 2004.

• ‘On Aspects of Bipolarity in Experience-Based Decision Making’. Invited Workshop ‘Repre-´ sentations Bipolaires en Raisonnement et Decision’´ , Foix (France), October 2003.

• ‘Sequential Decision Making and Case-Based Reasoning in Heuristic Search’. Invitational Workshop ‘Planning based on Decision Theory’, Udine (Italy), September 2002.

VII/XVI Research Seminars

• ‘Preference Learning: Methods and Algorithms for Ranking Problems’. Faculty of Computer Science, University of Ulm, January 2014.

• ‘Fuzzy Logic in Machine Learning: New Model Classes based on Generalized Aggregation Operators’. Seminar on ‘New Trends in Intelligent Systems and Soft Computing’, University of Granada, Spain, February 2012.

• ‘Machine Learning in Bioinformatics: Methods and Algorithms for Analyzing Structured Data’. GRID-Seminar, Giessen Research Center in Infectious Diseases, University of Giessen, December 2010.

• ‘Fuzzy Logic in Machine Learning’. Spring Pattern Recognition and Computer Vision Collo- quium. University of Prague, Czech Republic, April 2009.

• ‘Fuzzy Logic in Machine Learning’. University of Granada, Spain, March 2009.

• ‘Credible Case-Based Inference Using Similarity Profiles’. University de Lyon 1, France, November 2008.

• ‘On Binary Decomposition Techniques for Generalized Classification Problems’. Quantitia- tive Economic Colloquium, Freie Universitat¨ Berlin, Germany, June 2008.

• ‘Knowledge Discovery in Structured Data: Methods and Applications in Bioinformatics’. Uni- versity of Siegen, Germany, April 2008.

• ‘Knowledge Discovery in Structured Data: Methods and Applications in Bioinformatics’. Uni- versity of Trier, Germany, February 2008.

• ‘Fuzzy Sets in Data Mining: Useful or Not?’. Vienna Medical University, Austria, March 2007.

• ‘Knowledge Discovery in Protein Structure Databases: Graph Alignments for the Structural Analysis of Protein Binding Pockets’. Research seminar of the ‘Institut fur¨ Medizinische Biometrie und Epidemiologie’, Philipps-Universitat¨ Marburg, January 2006.

• ‘Fuzzy Data Mining in Bioinformatics’. Seminar ‘Einfuhrung¨ in moderne Methoden des Data Mining’, University of Gießen, June 2005.

• ‘Fuzzy Methods in Knowledge Discovery: On Frequency-Based Evaluation Measures in Fuzzy Data Mining’. Software Competence Center Hagenberg, Austria, April 2005.

• ‘Toward the Evaluation of Patterns in Fuzzy Data Mining’. Colloquium of the Graduate School of Knowledge Representation, Department of Computer Science, University of Leipzig, November 2004.

• ‘KI-Methoden in der Bioinformatik: Bayessche Netze zur Analyse von Genexpressionsdaten’. Mainzer KI-Kreis, Universitat¨ Mainz, Germany, November 2002.

TUTORIALS

• ‘Multi-Target Prediction’ (jointly with Willem Waegeman and Krzysztof Dembczynski). ICML– 2013, 30th International Conference on Machine Learning, Atlanta, USA, June 2013.

• ‘Preference Learning’ (jointly with Johannes Furnkranz).¨ ECAI–2012, 20th European Con- ference on Artificial Intelligence, Montpellier, France, August 2012.

• ‘Preference Learning’. ADT–2011, 2nd International Conference on Algorithmic Decision Theory, Rutgers University, New Jersey, USA, October 2011.

VIII/XVI • ‘Preference Learning’ (jointly with Johannes Furnkranz).¨ DS–2011, 14th International Con- ference on Discovery Science, Espoo, Finland, October 2011.

• ‘Preference Learning’ (jointly with Johannes Furnkranz).¨ ECML/PKDD–2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Barcelona, Spain, September 2010.

INVITED CONTRIBUTIONS

E. Hullermeier.¨ On the Usefulness of Fuzzy Sets in Data Mining. In R. Seising, editor, Views on Fuzzy Sets and Systems from Different Perspectives: Philosophy and Logic, Criticisms and Applications, pages 457–470, Springer-Verlag, 2009.

E. Hullermeier.¨ Fuzzy Methods in Data Mining. In J. Wang, editor, Encyclopedia of Data Warehousing and Mining — Second Edition, pages 907–912, Idea Group, Inc., Hershey, PA 17033-1240, USA, 2008.

E. Hullermeier.¨ Why Fuzzy Set Theory is Useful in Data Mining. In F. Masseglia, P.Poncelet, and M. Teisseire, editors, Successes and New Directions in Data Mining, pages 1–16, Idea Group, Inc., Hershey, PA 17033-1240, USA, 2007.

E. Hullermeier.¨ Granular Computing in Machine Learning and Data Mining. In W. Pedrycz, A. Skowron and V. Kreinovich, editors, Hanbook on Granular Computing, pages 889–906, John Wiley and Sons, 2008.

E. Hullermeier.¨ Fuzzy Methods in Data Mining: State of the Art and Prospects. In H. Bustince, F. Herrera, and J. Montero, editors, Fuzzy Sets and Their Extensions: Representation, Ag- gregation and Models. pages 355–374, Springer-Verlag, 2007.

E. Hullermeier.¨ Fuzzy Sets in Machine Learning and Data Mining: Status and Prospects. Special Issue of Fuzzy Sets and Systems on the occasion of the 40th anniversary of fuzzy sets.

J. Furnkranz¨ and E. Hullermeier.¨ Preference Learning. Kunstliche¨ Intelligenz (German AI Journal), volume 01/05, 2005.

D. Dubois, E. Hullermeier,¨ and H. Prade. Possibilistic Case-Based Decisions. In C. Lesage and M. Cottrell, editors, Connectionist Approaches in Economics and Management Sci- ences. Kluwer, 2003.

E. Hullermeier¨ and J. Beringer. Mining Implication-Based Fuzzy Association Rules in Databases. In B. Bouchon-Meunier, L. Foulloy, and R.R. Yager, editors, Intelligent Systems for Informa- tion Processing: From Representation to Applications. Elsevier, 2003.

E. Hullermeier.¨ Maschinelles Lernen und Komplexitat.¨ In F. Haslinger and P. Weise, edi- tors, Okonomie¨ und Gesellschaft, Jahrbuch 17: Komplexitat¨ und Lernen. Metropolis-Verlag, 2001.

E. Hullermeier,¨ D. Dubois, and H. Prade. Knowledge-Based Extrapolation of Cases: A Pos- sibilistic Approach. In B. Bouchon-Meunier, J. Gutierrez-Rios, L. Magdalena, and R.R. Yager, editors, Technologies for Constructing Intelligent Systems. Springer-Verlag, Berlin, 2001.

D. Dubois, E. Hullermeier,¨ and H. Prade. Formalizing Case-Based Inference using Fuzzy Rules. In S.K. Pal, D.Y. So, and T. Dillon, editors, Soft Computing in Case-Based Reasoning, pages 47–72. Springer-Verlag, Berlin, 2000.

IX/XVI E. Hullermeier.¨ Qualitatives Schließen und Qualitative Simulation. In H. Szczerbicka and T. Uthmann, editors, Modellierung, Simulation und Kunstliche¨ Intelligenz, pages 277–310. SCS Publishing House, Erlangen, 1999.

OTHER PROFESSIONAL ACTIVITIES

Co-Editor-in-Chief of “Fuzzy Sets and Systems” (Elsevier).

General chair of IPMU–2010, 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Dortmund, June/July 2010.

PC Co-Chair of ECML/PKDD–2014, European Conference on Machine Learning and Princi- ples and Practice of Knowledge Discovery in Databases, Nancy, France, September 2014; DS–2013, 16th International Conference on Discovery Science, Singapore, October 2013; SUM–2012, 6th International Conference on Scalable Uncertainty Management, Marburg, Germany, September 2012.

Editorial Board Member of the international journals “Machine Learning” (Springer, action editor), “IEEE Transactions on Fuzzy Systems”, “Soft Computing” (Springer, 2007–2013), “International Journal of Approximate Reasoning” (Elsevier), “Advances in Data Analysis and Classification” (Springer, associate editor), “International Journal of Uncertainty, Fuzzi- ness and Knowledge-Based Systems”, “Evolving Systems” (Springer, associate editor), “In- ternational Journal of Data Mining, Modelling and Management” (associate editor), “Interna- tional Journal of Computational Intelligence Research”, “International Journal on Advances in Fuzzy Systems” (associate editor), “Journal of Data Mining and Digital Humanities”, “In- ternational Journal of Intelligent Engineering Informatics”, “Intelligent Decision Technology: An International Journal”, “Progress in Artificial Intelligence”, “MDPI Biology”, “Journal of In- telligent Systems”, “Open Applied Informatics Journal”, “International Journal of Information Engineering”, “Annals of Fuzzy Sets, Fuzzy Logic and Fuzzy Systems”, “International Jour- nal of Applied and Industrial Mathematics”, “International Journal of Industrial Mathematics”, “Fuzzy Set valued Analysis”, “Archives for the Philosophy and History of Soft Computing”.

Board Member of EUSFLAT (European Society for Fuzzy Logic and Technology).

Head of the IEEE CIS Task Force on Machine Learning.

Member of IEEE CIS Fuzzy Systems Technical Committee, IEEE-CIS Emerging Technologies Technical Committee.

Coordinator of the EUSFLAT Working Group on “Learning and Data Mining”.

Vice Chairman of the GMA Technical Committee ‘Computational Intelligence’ (VDI/VDE Ge- sellschaft fur¨ Mess- und Automatisierungstechnik, Fachausschuss 5.14).

Scientific Advisor of the Iranian Society of Fuzzy Sets and Systems (IFSS)

Co-Organizer of special sessions/tracks: “Computational Intelligence in Machine Learn- ing and Data Mining” at WCCI-2010, IEEE World Congress on Computational Intelligence (Barcelona, 2010); “Fuzzy Sets in Computational Biology” at IFSA-EUSFLAT–2009, In- ternational Fuzzy Systems Association World Congress and European Society for Fuzzy Logic and Technology Conference (Lisbon, Portugal, July 2009); “Machine Learning and Data Mining” at IFSA-EUSFLAT–2009, International Fuzzy Systems Association World Congress and European Society for Fuzzy Logic and Technology Conference (Lisbon, Por- tugal, July 2009); “Uncertainty in Machine Learning and Data Mining” at IPMU–2008, 12th International Conference on Information Processing and Management of Uncertainty in

X/XVI Knowledge-Based Systems (Malaga, Spain, July 2008); “Intelligent Data Analysis” at FUZZ- IEEE-2007, IEEE International Conference on Fuzzy Systems (London, 2007); “Fuzzy Methods and Models in Learning from Data” at FUZZ-IEEE-2004, IEEE International Con- ference on Fuzzy Systems (Budapest, Hungary, 2004); “Ranking, Multi-Label Classification, Preferences” at GFKL–2005, 29th Annual Conference of the German Classification Soci- ety (Magdeburg, March 2005); “Fuzzy Sets and Possibility Theory in Machine Learning and Data Mining” at IPMU–2002, 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (Annecy, France, July 2002).

Co-Organizer of workshops: “Preference Learning: Problems and Application in Artificial Intelligence” (part of ECAI– 2012, European Conference on Artificial Intelligence, Montpellier, France, August 2012); “Preference Learning” (part of ECML/PKDD–2010, European Conference on Machine Learn- ing and Principles and Practice of Knowledge Discovery in Databases, Barcelona, Spain, July 2010); “Preference Learning” (part of ECML/PKDD–2009, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bled, Slovenia, September 2009); “Uncertainty, Knowledge Discovery and Similarity in Case- Based Reasoning” (part of 8th International Conference on Case-Based Reasoning, Seattle, Washington, USA, July 2009); “Uncertainty, Similarity, and Knowledge-Discovery in Case- Based Reasoning” (part of 9th European Conference on Case-Based Reasoning, Trier, Ger- many, September 2008); “Preference Learning” (part of ECML/PKDD–2008, European Con- ference on Machine Learning and Principles and Practice of Knowledge Discovery in Da- tabases, Antwerp, Belgium, September 2008); “Uncertainty and Fuzziness in Case-Based Reasoning” (part of 7th International Conference on Case-Based Reasoning, Belfast, North- ern Ireland, August 2007); “Uncertainty and Fuzziness in Case-Based Reasoning” (part of 8th European Conference on Case-Based Reasoning, Ol¨ udeniz/Fethiye,¨ Turkey, September 2006); “Symposium on Fuzzy Systems in Computer Science 2006” (Magdeburg, Septem- ber 2006); “Soft Computing for Information Mining” (part of 27th German Conference on Artificial Intelligence, Ulm, September 2004); “Preference Learning: Models, Methods, Ap- plications” (part of 26th German Conference on Artificial Intelligence, Hamburg, September 2003); “Entscheiden bei unvollstandiger¨ Information: Neuere Methoden und Anwendun- gen” (part of GI-Jahrestagung, Conference of the German Informatics Society, Dortmund, September 2002); RA` PC–2000, French Workshop on Case-Based Reasoning (Toulouse, May 2000); “Simulation in wissensbasierten Systemen” (Paderborn, April 1998); “Model- lierung und Simulation technischer Systeme” (Paderborn, June 1997).

Member of Program Committee: Area chair: International Conference on Machine Learning (ICML, Haifa, Israel, 2010), AAAI Conference on Artificial Intelligence (AAAI, Toronto, Canada, 2012), European Conference on Machine Learning (ECML, Prague, 2013; Bristol, UK, 2012). Neural Information Processing Systems (NIPS, Lake Tahoe, Nevada, USA, 2012, 2013), SIAM International Conference on Data Mining (SDM, Anahemin, California, USA, 2012), 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD, Kuala Lumpur, Malaysia, 2012; Tainan, Taiwan, 2014), International Conference on Pattern Recog- nition Applications and Methods (ICPRAM, Vilamoura, Portugal, 2012), IEEE Symposium Series on Computational Intelligence (SSCI, Paris, 2011; Singapore 2013), International Conference on Algorithmic Decision Theory (ADT, Brussels, 2013), 14/15th International Conference on Discovery Science (Espoo, Finland, 2011; Lyon, France, 2012), Interna- tional Joint Conference on Artificial Intelligence (IJCAI, Beijing, 2013), 25/26th Conference on Artificial Intelligence (AAAI, San Francisco, 2011; Toronto, Canada, 2012 (Area Chair); Bellevue, Washington, 2013), European Conference on Artificial Intelligence (ECAI, Riva Del Garda, Italy, 2006; Lisbon, Portugal, 2010; Barcelona, Spain, 2011 (Senior PC Mem-

XI/XVI ber); Montpellier, France, 2012), ACM International Conference on Information and Know- ledge Management (CIKM, Toronto, Canada, 2010; Glasgow, Scotland, UK, 2011), 11th International Conference on Intelligent Systems Design and Applications (ISDA, Cordoba, Spain, 2011), 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE, 2010, Cordoba, Spain), 2nd International Sympo- sium on Intelligent Decision Technologies and 3rd International Symposium on Intelligent and Interactive Multimedia: Systems and Services (KES-IDT, 2010, Baltimore, USA), 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD, London, UK, 2010), IEEE International Con- ference on Data Mining (IEEE ICDM, 2012, Brussels, Belgium; 2010, Sydney, Australia; 2009, Miami, Florida), IFSA (International Fuzzy Systems Association) World Congress (Edmonton, Canada, 2013; Lisbon, 2009, track chair), International Conference on Ma- chine Learning (ICML, Beijing 2014, Atlanta 2013, Edinburgh 2012, Haifa 2010 (area chair), Montreal 2009, Helsinki 2008), International Symposium on Intelligent Data Analysis (IDA, London, 2013; Helsinki, Finland, 2012; Porto, Portugal, 2011; Lyon, France, 2009), IEEE Congress on Evolutionary Computation (CEC, Track on Evolutionary Computation in Bioin- formatics and Computational Biology, Trondheim, Norway, 2009) , International Symposium on Computer and Information Sciences (ISCIS, AI, Machine Learning and Data Mining Track, Cyprus, 2009), International Conference on Knowledge Engineering and Ontology Development (KEOD, Madeira, 2009), International Conference on Knowledge Science, Engineering and Management (5th KSEM, Irvine, California; 4th KSEM, Belfast, Norther Ireland, UK, 2010), International Conference on Adaptive and Intelligent Systems (ICAIS, Klagenfurt, Austria, 2011), 9th International Conference on Flexible Query Answering Sys- tems (FQAS, Ghent, Belgium, 2011), 6th/7th International Symposium on Foundations of Information and Knowledge Systems (FoIKS, Sofia, Bulgaria, 2010; Kiel, Germany, 2012); Second International Conference on Agents and Artificial Intelligence (ICAART, Valencia, Spain, 2010); German Conference on Artificial Intelligence (KI, Paderborn, 2009), Indian International Conference on Artificial Intelligence (IICAI, Tumkur, India, 2009; Tumkur, In- dia, 2011); European Conference on Machine Learning (ECML, Berlin 2006; Warsaw 2007; Antwerp 2008; Bled, Slovenia 2009; Barcelona 2010; Bristol 2012; Prague 2013), Euro- pean Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD, Porto 2005; Berlin 2006; Warsaw 2007; Antwerp 2008), European Conference on Sym- bolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU, Barcelona 2005; Hammamet 2007; Verona 2009; Belfast 2011), International Conference on Case- Based Reasoning (ICCBR, Dublin 2007; Seattle 2009; Alessandria, Italy, 2010), European Conference on Case-Based Reasoning (ECCBR, Trier, Germany, 2008), International Con- ference on Scalable Uncertainty Management (SUM, Washington DC, 2007; Napoli, Italy, 2008; Washington, 2009; Dayton, Ohio, USA), Industrial Conference on Data Mining (ICDM, Leipzig, Germany, 2007, 2008, 2009, Berlin 2010), International Conference on Soft Meth- ods in Probability and Statistics (SMPS, Toulouse, France, 2008; Mieres, Spain, 2010; Kon- stanz, Germany, 2012), IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB, Sun Valley Resort, Sun Valley, Idaho, 2008; Montreal, Vanada, 2010; San Diego, California, 2012), International Conference on Parallel Problem Solving From Nature (PPSN, Dortmund, Germany, 2008, Krakow, Poland, 2010), Eurofuse International Workshop (Jaen,´ Spain, 2007), Conference of the European Society for Fuzzy Logic and Technology (Eusflat, Ostrava, Czech Republic, 2007; Aix-Les-Bains, France, 2011; Milan, Italy, 2013), International Conference on Fuzzy Systems and Knowledge Dis- covery (FSKD, Haikou, China, 2007; Jinan, China, 2008), Conference of the North American Fuzzy Information Processing Society (NAFIPS, San Diego, California, 2007), Extraction et Gestion des Connaissances (EGC, Brest, France, 2011), International Workshop Mod- elling and Reasoning in Context (MRC, Roskilde, Denmark, 2007; Delft, The Netherlands, 2008; Lisbon, Portugal, 2010), International Joint Conference on Knowledge Management

XII/XVI for Composite Materials (KMCM, Dusseldorf,¨ Germany, 2007), International Conference on Data Warehousing and Knowledge Discovery (DaWaK, Krakow, Poland, 2006; Turin, Italy, 2008), International Conference on Natural Computation and International Conference on Fuzzy Systems and Knowledge Discovery (ICNC-FSKD, Hunan, China, 2005; Xian, China, 2006; Donghua, Shanghai, China, 2011), International Symposium on Fuzzy and Rough Sets (ISFUROS, Santa Clara, Cuba, 2006), Special Issue of the Journal Data & Knowledge Engineering (Elsevier) on “Intelligent Data Mining” (2005), Joint special issue of ARIMA and South African Computer Journal (SACJ) on “Advances in end-user data mining techniques” (2005), IEEE International Conference on Fuzzy Systems (Brisbane, Australia, 2012; Taipei, Taiwan, 2011; Barcelona, Spain, 2010; Vancouver, Canada, 2006; Reno, Nevada, 2005; Budapest, Hungary, 2004; Honululu, Hawaii, 2002), International Conference on Computa- tional Intelligence (Dortmund, Germany, 2006, 2004, 2001), 3rd International Conference on Machine Learning and Cybernetics (ICMLC, Shanghai, China, 2004),

Member of Program Committee (Workshops): ECAI–2012 Workshop on Cooking with Computers (CWC), Montpellier, France, 2012; ECAI–2012 Workshop on Similarity and Analogy-based Methods in AI (SAMAI), Montpellier, France, 2012; NIPS–2011 Workshop on Choice Models and Preference Learning (CMPL), Granada, Spain, 2011; EUROFUSE Workshop on Fuzzy Methods for Knowledge-Based Systems, Regua,´ Portugal, 2011; Arti- ficial Intelligence and Logistics (AILog, part of IJCAI), Barcelona, Spain, 2011; 13th Inter- national Workshop on Non-Monotonic Reasoning–NMR and Uncertainty, Totonto, Canada, 2010; Machine Learning in Real-time Applications, MlRTA09 (part of 32nd Annual Confe- rence on Artificial Intelligence, KI-2009), Paderborn, Germany, 2009; Soft approaches to information access on the Web (part of IEEE/WIC/ACM International Conference on Web Intelligence, WI-09) Milan, Italy, 2009; Joint 5th German Workshop on Experience Man- agement and Enterprise Search (GWEM-ES, Solothurn, Switzerland, 2009), Workshop on Computational Intelligence in Data Mining (part of IEEE International Conference on Data Mining, ICDM), New Orleans, 2005; Workshop on Alternative Techniques for Data Mining (part of IEEE International Conference on Data Mining, ICDM), Brighton, UK, 2004; Inter- national Workshop on Soft Computing in Case-Based Reasoning (part of 4th International Conference on Case-Based Reasoning, ICCBR), Vancouver, Canada, 2001.

Co-Organizer of the Data and Knowledge Engineering Research Colloquium at the University of Magdeburg (12/2004–12/2006)

Co-Organizer of the Computational Biology Research Colloquium within the SynMikro Re- search Center for Synthetic Microbiolgy, University of Marburg (since 09/2010)

Member of the Organizing Committee of “Interdisziplinares¨ Statistik-Kolloquium der Univer- sitaten¨ Gießen und Marburg” (2002–2004)

Reviewer for European Union (FP 7), Deutsche Forschungsgemeinschaft (DFG, German Re- search Foundation), The Netherlands Organisation for Scientific Research (NWO), United States–Israel Binational Science Foundation (BSF), Czech Science Foundation (GACR), Research Foundation Flanders (FWO), Ben-Gurion University of the Negev (promotion to associate/full professor), Theory and Decision Library (Kluwer Academic Publishers), Dis- crete Applied Mathematics, the Computer Journal, International Journal of Artificial Intelli- gence, Journal of Artificial Intelligence Research, Journal of Machine Learning Research, Machine Learning, ACM Transaction on Knowledge Discovery in Data, Information Retrieval, Fuzzy Sets and Systems, Annals of Operations Research, ACM Transactions on the Web, Statistica Sinica, IEEE Transactions on Computers, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man and Cybernetics, IEEE Transactions on Knowledge and Data Engineering, IEEE/ACM Transactions on Computational Biology and Bioinfor- matics, IEEE Software, IEEE Computational Intelligence Magazine, Pattern Recognition,

XIII/XVI Pattern Recognition and Machine Intelligence, Pattern Recognition Letters, Bioinformat- ics, BMC Bioinformatics, AI Communications, European Journal of Operational Research, Acta Automatica Sinica, Knowledge and Information Systems, International Journal of Un- certainty, Fuzziness and Knowledge-Based Systems, International Journal of Approximate Reasoning, International Journal of Artificial Intelligence in Medicine, Computational Statis- tics and Data Analysis, International Journal of Machine Learning and Cybernetics, Evolving Systems, PLoS ONE, Molecular BioSystems, ACM Transactions on Multimedia Computing, Communications and Applications, Decision Support Systems, Journal of Intelligent and Fuzzy Systems, International Journal of Fuzzy Sets, Applied Intelligence, Data and Know- ledge Engineering, The VLDB Journal, Mathematical Methods of Operations Research, Applied Soft Computing, Applied Artificial Intelligence, Engineering Applications of Artifi- cial Intelligence, Information Sciences, Journal of Algorithms in Cognition, Informatics, and Logic, International Journal of Intelligent Systems, International Journal of Production Eco- nomics, Journal of Integrated Computer-Aided Engineering, TEST, 4OR–a Quarterly Jour- nal of Operations Research, Computers and Operations Research, International Journal of Computer Mathematics, Journal of Multiple-Valued Logic and Soft Computing, Fuzzy Eco- nomic Review, Automatisierungstechnik, Encyclopedia of Data Warehousing and Mining, International Joint Conference on Artificial Intelligence, European Conference on Artificial Intelligence, European Conference on Machine Learning, International Conference on Un- certainty in Artificial Intelligence, International Conference on Knowledge Representation, International Conference on Theoretical Computer Science, International Conference on Data Engineering, International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, European Conference on Principles and Prac- tice of Knowledge Discovery in Databases, International Conference on Computational In- telligence, ACM/SIGMOD Symposium on Principles of Database Systems, IEEE Interna- tional Conference on Fuzzy Systems, European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, International Symposium on Foundations of In- formation and Knowledge Systems, International FLINS Conference on Applied Artificial In- telligence, European Conference on Fuzzy Logic and Technology, Computability in Europe, Annual Conference of the German Classification Society, GI-Fachtagung fur¨ Datenbanksys- teme in Business, Technologie und Web.

Director of the Institute for Technical and Business Information Systems (≈ 70 employees), Faculty of Computer Science, University of Magdeburg (03/2005–12/2006).

Self-administration: Member of the Faculty Council (2009–2010). Co-advisor of master program in Data and Knowledge Engineering (01/2005–12/2006). Member of ≈ 20 PhD committees. Member of committee for evaluation of a junior professorship (Magdeburg Uni- versity, 2005). Member of an appointment committee (Marburg University, 2007).

Supervised PhD Theses:

– ‘Label Ranking with Probabilistic Models’, by Weiwei Cheng, Philipps-Universitat¨ Mar- burg, defended May 2012. – ‘Graph-based Approaches to Protein Structure Comparison—From Local to Global Sim- ilarity’, by Marco Mernberger, Philipps-Universitat¨ Marburg, defended December 2011. – ‘Induction and Fuzzification of Classification Rules’, by Jens Christian Huhn,¨ Philipps- Universitat¨ Marburg, defended December 2009. – ‘Ranking and Reliable Classification’, by Stijn Vanderlooy, Maastricht University, de- fended July 2009. – ‘Fuzzy Operator Trees for Modelling Rating Functions’, by Yu Yi, Philipps-Universitat¨ Marburg, defended December 2008.

XIV/XVI – ‘Online Data Mining auf Datenstrmen: Methoden zur Clusteranalyse und Klassifikation’, by Jurgen¨ Beringer, Otto-von-Guericke-Universitat¨ Magdeburg, defended August 2007. – ‘Efficient Algorithms for Robust Pattern Mining on Structured Objects with Applications to Structure-Based Drug Design’, by Nils Weskamp, Philipps-Universitat¨ Marburg, de- fended February 2007.

Reviewer (Co-Supervisor) of PhD Theses:

–‘M ethodes´ non-parametriques´ pour la prevision´ d’intervalles avec haut niveau de con- fiance: application a` la prevision´ de trajectoire d’avions’, by Mohammed Ghasemi Hamed, Universite´ de Toulouse, 2014. – ‘Orthology-based approaches and applications for comparative genomics’, by Marcus Lechner, Department of Pharmacy, Philipps-Universitat¨ Marburg, 2013. – ‘Studies in Learning Monotone Models from Data’, by Nicola Barile, University of Utrecht, 2013. – ‘Representation´ et extraction automatique de connaissances semantiques´ et emotionnelles´ pour linterpretation´ de donnees’,´ by Marie-Jeanne Lesot, HDR (habilitation a` diriger des recherches), Universite´ Piere et Marie Curie, Paris, 2013. – ‘Inference and Application of Likelihood Based Methods for Hidden Markov Models’ by Florian Schwaiger, Philipps-Universitat¨ Marburg, 2013. – ‘De la Consistence des Formulations de Substitution Convexes pour l’Ordonnancement’, by Clment Calauzenes,` Universit Pierre et Marie Curie, Paris, 2013. – ‘Representation´ et apprentissage a` partir de textes pour des informations emotionnelles´ et pour des informations dynamiques’, by Fabon Dzogang, Universit Pierre et Marie Curie, Paris, 2013. – ‘Trimming the Complexity of Ranking by Pairwise Comparison’, by Samuel Hiard, Uni- versity of Liege,` 2013. –‘ Elicitation´ indirecte de modeles de tri multicritere’,` by Olivier Cailloux, Laboratoire Genie´ Industriel, Ecole´ Cenrale Paris, 2012. – ‘Knowledge Representation with Condensed Set-Valued Attributes’, by Frank Christo- pher Rugheimer,¨ Faculty of Computer Science, University of Magdeburg, Germany, 2012. – ‘Efficient Pairwise Multilabel Classification’, by Eneldo Loza Mencia, Department of Computer Science, Technical University Darmstadt, Germany, 2012. – ‘Efficient Decomposition-Based Multiclass and Multilabel Classification’, by Sang-Hyeun Park, Department of Computer Science, Technical University Darmstadt, Germany, 2012. – ‘Development and Improvement of Tools and Algorithms for the Problem of Atom Type Perception and for the Assessment of Protein-Ligand-Complex Geometries’, by Gerd Neudert, Department of Pharmacy, Philipps-Universitat¨ Marburg, 2012. – ‘Kernel-based Ranking: Methods for Learning and Performance Estimation’, by Antti Airola, Department of Information Technology, University of Turku, Finland, 2011. – ‘Application de la theorie´ de la revision´ des connaissances au raisonnement a` partir de cas’, by Julien Cojan, Universite´ Henri Poincare,´ Nancy 1, France, 2011. – ‘Knowledge-based Optimization of Protein-Ligand-Complex Geometries’, by Andreas Spitzmuller,¨ Department of Pharmacy, Philipps-Universitat¨ Marburg, 2011. – ‘Analysis of Genomic and Proteomic Signals Using Signal Processing and Soft Com- puting Techniques’, by Sitanshu Sekhar Sahu, National Institute of Technology (NIT), Rourkela, India, 2011.

XV/XVI – ‘Systeme` personnalise´ de planification ditineraire´ unimodal: Une approche basee´ sur la theorie´ des ensembles flous’, by Amine Mokhtari, Universite´ de Rennes 1, France, 2011. – ‘Apprentissage Artificiel et Raisonnement Flou’, by Christophe Marsala, HDR (habilita- tion a` diriger des recherches), Universite´ Piere et Marie Curie, Paris, 2010. –‘D etermination´ Automatique des Fonctions d’Appartenance et Interrogation Flexible et Cooperative des Bases de Donnees’,´ by Narjes Hachani Gharbi, Universite´ Tunis El Manar, Tunisia, 2010. – ‘Learning to Rank: a ROC-Based Graph-Theoretic Approach’, by Willem Waegeman, Gent University, Belgium, 2008. –‘D etection´ de nouveaute´ dans le cadre de la theorie´ des fonctions de croyance. Ap- plication a` la surveillance de proced´ es´ d’incineration´ de d’echets’, by Astride Aregui, Universite´ de Technologie de Compiegne,` France, 2007. – ‘Extraction de Sequences´ Frequentes:´ Des Donnees´ Numeriques´ aux Valeurs Man- quantes’, by Celine´ Fiot, Universite´ Montpellier II, 2007. – ‘Knowledge Extraction and Summarization for Textual Case-Based Reasoning’, by Eni- ana Mustafaraj, Philipps-Universitat¨ Marburg, 2007. – ‘Time Series Knowledge Mining’ by Fabian Morchen,¨ Philipps-Universitat¨ Marburg, 2006. – ‘Concepts to Interfere with Protein-Protein Complex Formations: Data Analysis, Struc- tural Evidence and Strategies for Finding Small Molecule Modulators’ by Peter Block, Philipps-Universitat¨ Marburg, 2005. – ‘Programmation Logique Inductive Floue et Possibiliste: Gagner en Expressivite,´ Adapt- abilite,´ ou en Efficacite’´ by Mathieu Serrurier, Universite´ Paul Sabatier, Toulouse, 2005. – ‘Risk Management of Natural Disasters: A Fuzzy-Probabilistic Methodology and its Ap- plication to Seismic Hazards’ by Iman Karimi, RWTH Aachen, 2005. – ‘Gene Expression Data Analysis Using Novel Methods: Predicting Time-Delayed Cor- relations and Evolutionary Conserved Functional Modules’ by Rajarajeswari Balasub- ramaniyan, Max-Planck-Institute for Terrestrial Microbiology, Marburg, 2005. – ‘Data-Driven Incremental Learning of Takagi-Sugeno Fuzzy Models’ by Edwin Lughofer, Johannes Kepler Universitat¨ Linz, 2005. – ‘Active Learning with Kernel Machines’ by Klaus Brinker, Universitat¨ Paderborn, 2005. – ‘Beschreibung von Proteinbindetaschen fur¨ Funktionsstudien und de Novo-Design und die Entwicklung von Methoden zur funktionellen Klassifizierung von Proteinfamilien’ by Daniel Kuhn, Department of Pharmacy, Philipps-Universitat¨ Marburg, 2004. – ‘Intelligente Methoden fur¨ mechatronische Systeme in der Nahrungsmittelindustrie’ by Stephan Strelen, Philipps-Universitat¨ Marburg, 2004. – ‘Some studies on uncertainty management in dynamical systems using fuzzy tech- niques with applications’ by Kaushik Majumdar, Indian Statistical Institute, 2003.

MEMBERSHIP OF SCIENTIFIC SOCIETIES

– EUSFLAT, European Society for Fuzzy Logic and Technology – IEEE Computational Intelligence Society – GFKL, Gesellschaft fur¨ Klassifikation – GI, Gesellschaft fur¨ Informatik (German Informatics Society)

XVI/XVI PUBLICATION LIST 02/2014

Eyke H ¨ullermeier Department of Mathematics and Computer Science Eyke University of Marburg, Germany

1 MONOGRAPHS

[1]E.H ullermeier.¨ Case-Based Approximate Reasoning, volume 44 of Theory and Decision Library, Series B: Mathematical and Statistical Methods. Springer-Verlag, 2007. 370 pages.

2 EDITED VOLUMES AND SPECIAL ISSUES

[1]E.H ullermeier,¨ S. Link, T. Fober, and B. Seeger, editors. Scalable Uncertainty Management (Proceedings SUM–2012). Number 7520 in Lecture Notes in Artificial Intelligence. Springer-Verlag, 2012.

[2] J. Furnkranz¨ and E. Hullermeier.¨ Special Issue on Preference Learning. Machine Learning. Forthcoming.

[3] C. Domshlak, E. Hullermeier,¨ S. Kaci, and H. Prade. Special Issue on Representing, Processing, and Learning Preferences: Theoretical and Practical Challenges. Artificial Intelligence, 175(7–8):1037–1478, 2011.

[4] J. Furnkranz¨ and E. Hullermeier,¨ editors. Preference Learning. Springer-Verlag, 2011. [5]E.H ullermeier,¨ R. Kruse, and F. Hoffmann, editors. Computational Intelligence for Knowledge-Based Systems Design. Number 6178 in Lecture Notes in Artificial Intelligence. Springer-Verlag, 2011.

[6]E.H ullermeier,¨ R. Kruse, and F. Hoffmann, editors. Proceedings of the 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Part I + II. Springer-Verlag, 2010.

[7] F. Hoffmann and E. Hullermeier,¨ editors. Proceedings 23. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe, Germany, 2013.

[8] F. Hoffmann and E. Hullermeier,¨ editors. Proceedings 22. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe, Germany, 2012.

[9] F. Hoffmann and E. Hullermeier,¨ editors. Proceedings 21. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe, Germany, 2011.

[10] F. Hoffmann and E. Hullermeier,¨ editors. Proceedings 20. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe, Germany, 2010.

[11] F. Hoffmann and E. Hullermeier,¨ editors. Proceedings 19. Workshop Computational Intelligence. KIT Scientific Publishing, Karlsruhe, Germany, 2009.

I/XIX [12]E.H ullermeier¨ and J. Furnkranz.¨ Proceedings PL–09, Workshop on Preference Learning at ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Bled, Slovenia, 2009.

[13]E.H ullermeier¨ and J. Furnkranz.¨ Proceedings PL–08, Workshop on Preference Learning at ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in Databases. Antwerp, Belgium, 2008.

[14]E.H ullermeier,¨ F. Klawonn, and A. Nurnberger.¨ Special Issue on Fuzzy Methods in Machine Llearning and Data Mining. International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 15(5), 2007.

[15]E.H ullermeier,¨ R. Kruse, A. Nurnberger,¨ and J. Strackeljan, editors. Proceedings FSCS 2006, Symposium on Fuzzy Systems in Computer Science. 2006.

[16]E.H ullermeier.¨ Special Issue on Fuzzy Sets in Knowledge Discovery. Fuzzy Sets and Systems, 149(1), 2005.

[17] U. Bodenhofer, E. Hullermeier,¨ F. Klawonn, and R. Kruse. Special Issue on Soft Computing for Information Mining. Soft Computing, 11(5), 2007.

3 JOURNAL PUBLICATIONS

[1] M. Mernberger, D. Moog, S. Stork, S. Zauner, U. Maier, and E. Hullermeier.¨ Protein sub-cellular localization prediction for specialized compartments via optimized time series distances. Journal of Bioinformatics and Computational Biology, 12(1), 2014.

[2] S. Bosner,¨ K. Bonisch,¨ J. Haasenritter, P. Schlegel, E. Hullermeier,¨ and N. Donner-Banzhoff. Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? – A cross sectional study. BMC Family Practice, 14(1):154–162, 2013. [3] R. Senge, S. Bosner,¨ K. Dembczynski, J. Haasenritter, O. Hirsch, N. Donner-Banzhoff, and E. Hullermeier.¨ Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Information Sciences, 255:16–29, 2014.

[4] N. Donner-Banzhoff, J. Haasenritter, E. Hullermeier,¨ A. Viniol, S. Bosner,¨ and A. Becker. The comprehensive diagnostic study is suggested as a design to model the diagnostic process. Journal of Clinical Epidemiology, 2014. In press.

[5] A. Fallah Tehrani, W. Cheng, K. Dembczynski, and E. Hullermeier.¨ Learning monotone nonlinear models using the Choquet integral. Machine Learning, 89(1):183–211, 2012.

[6] H. Bustince, M. Pagola, R. Mesiar, E. Hullermeier,¨ and F. Herrera. Grouping, overlap, and generalized bientropic functions for fuzzy modeling of pairwise comparisons. IEEE Transactions on Fuzzy Systems, 20(3):405–415, 2012.

[7] J. Haasenritter, A. Viniol, A. Becker, S. Bosner,¨ E. Hullermeier,¨ R. Senge, and N. Donner-Banzhoff. Diagnose im Kontext—eine erweiterte Perspektive (Diagnosis in context—broadening the perspective). Zeitschrift fur¨ Evidenz, Fortbildung und Qualitat¨ im Gesundheitswesen (ZEFQ), 107:585–591, 2013.

[8] J. Furnkranz,¨ E. Hullermeier,¨ W. Cheng, and S.H. Park. Preference-based reinforcement learning: A formal framework and a policy iteration algorithm. Machine Learning, 89(1):123–156, 2012.

II/XIX [9] K. Dembczynski, W. Waegeman, W. Cheng, and E. Hullermeier.¨ On label dependence and loss minimization in multi-label classification. Machine Learning, 88(1–2):5–45, 2012. Honorable mentioning in the list of notable computing items 2012 by the ACM. [10] A. Shaker and E. Hullermeier.¨ IBLStreams: A system for instance-based classification and regression on data streams. Evolving Systems, 3(4):235–249, 2012. [11] M. Dolorez Ruiz and E. Hullermeier.¨ A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. Information Sciences, 206:1–17, 2012. [12] A. Fallah Tehrani, W. Cheng, and E. Hullermeier.¨ Preference learning using the Choquet integral: The case of multipartite ranking. IEEE Transactions on Fuzzy Systems, 20(6):1102–1113, 2012. [13] T. Fober, M. Mernberger, G. Klebe, and E. Hullermeier.¨ Fingerprint kernels for protein structure comparison. Molecular Informatics, 31(6–7):443–452, 2012. [14]E.H ullermeier,¨ M. Rifqi, S. Henzgen, and R. Senge. Comparing fuzzy partitions: A generalization of the Rand index and related measures. IEEE Transactions on Fuzzy Systems, 20(3):546–556, 2012. [15] R. Senge, T. Fober, N. Nasiri, and E. Hullermeier.¨ Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. at–Atomatisierungstechnik, 60(10):622–629, 2012. [16] O. Hirsch, S. Bosner,¨ E. Hullermeier,¨ R. Senge, K. Dembczynski, and N. Donner-Banzhoff. Multivariate modeling to identify patterns in clinical data: The example of chest pain. BMC Medical Research Methodology, 11(155), 2011. [17] P. Pfeffer, T. Fober, E. Hullermeier,¨ and G. Klebe. GARLig: A fully automated tool for subset selection of large fragment spaces via a self-adaptive genetic algorithm. Journal of Chemical Information and Modeling, 50(9):1644–1659, 2010. [18] A. Shaker, R. Senge, and E. Hullermeier.¨ Evolving fuzzy pattern trees for binary classification on data streams. Information Sciences, 220:34–45, 2013. [19] C. Domshlak, E. Hullermeier,¨ S. Kaci, and H. Prade. Preferences in AI: An overview. Artificial Intelligence, 175(7–8):1037–1052, 2011. [20]E.H ullermeier.¨ Fuzzy machine learning and data mining. WIREs Data Mining and Knowledge Discovery, 1(4):269–283, 2011. [21] M. Mernberger, G. Klebe, and E. Hullermeier.¨ SEGA: Semi-global graph alignment for structure-based protein comparison. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(5):1330–1343, 2011. [22] T. Fober, S. Glinca, G. Klebe, and E. Hullermeier.¨ Superposition and alignment of labeled point clouds. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(6):1653–1666, 2011. [23]E.H ullermeier.¨ Fuzzy sets in machine learning and data mining. Applied Soft Computing Journal, pages 1493–1505, 2011. [24]E.H ullermeier¨ and S. Vanderlooy. Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognition, 43(1):128–142, 2010. [25]E.H ullermeier¨ and J. Furnkranz.¨ On predictive accuracy and risk minimization in pairwise label ranking. Journal of Computer and System Sciences, 76(1):49–62, 2010. [26] R. Senge and E. Hullermeier.¨ Top-down induction of fuzzy pattern trees. IEEE Transactions on Fuzzy Systems, 19(2):241–252, 2011.

III/XIX [27] N. Weskamp, E. Hullermeier,¨ and G. Klebe. Merging chemical and biological space: Structural mapping of enzyme binding pocket space. Proteins, 76(2):317–330, 2009.

[28] J. Huhn¨ and E. Hullermeier.¨ FURIA: An algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery, 19:293–319, 2009.

[29]E.H ullermeier¨ and S. Vanderlooy. Why fuzzy decision trees are good rankers. IEEE Transactions on Fuzzy Systems, 17(6):1233–1244, 2009.

[30] W. Cheng and E. Hullermeier.¨ Combining instance-based learning and logistic regression for multilabel classification. Machine Learning, 76(2–3):211–225, 2009.

[31] Y. Yi, T. Fober, and E. Hullermeier.¨ Fuzzy operator trees for modeling rating functions. International Journal of Computational Intelligence and Applications, 8(4):413–428, 2009.

[32] T. Fober, M. Mernberger, G. Klebe, and E. Hullermeier.¨ Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. Bioinformatics, 25(16):2110–2117, 2009.

[33] J. Huhn¨ and E. Hullermeier.¨ FR3: A fuzzy rule learner for inducing reliable classifiers. IEEE Transactions on Fuzzy Systems, 17(1):138–149, 2009.

[34] J. Huhn¨ and E. Hullermeier.¨ Is an ordinal class structure useful in classifier learning? International Journal of Data Mining, Modeling and Management, 1(1):45–67, 2008.

[35]E.H ullermeier,¨ I. Vladimirskiy, B. Prados Suarez, and E. Stauch. Supporting case-based retrieval by similarity skylines: Basic concepts ad extensions. Kunstliche¨ Intelligenz, 1/09:24–29, 2009.

[36]E.H ullermeier,¨ J. Furnkranz,¨ W. Cheng, and K. Brinker. Label ranking by learning pairwise preferences. Artificial Intelligence, 172:1897–1917, 2008.

[37] S. Vanderlooy and E. Hullermeier.¨ A critical analysis of variants of the AUC. Machine Learning, 72:247–272, 2008.

[38] J. Furnkranz,¨ E. Hullermeier,¨ E. Mencia, and K. Brinker. Multilabel classification via calibrated label ranking. Machine Learning, 73(2):133–153, 2008.

[39]E.H ullermeier¨ and K. Brinker. Learning valued preference structures for solving classification problems. Fuzzy Sets and Systems, 159(18):2337–2352, 2008.

[40]E.H ullermeier¨ and Y. Yi. In defense of fuzzy association analysis. IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, 37(4):1039–1043, 2007.

[41]E.H ullermeier.¨ Credible case-based inference using similarity profiles. IEEE Transactions on Knowledge and Data Engineering, 19(5):847–858, 2007.

[42] J. Beringer and E. Hullermeier.¨ Efficient instance-based learning on data streams. Intelligent Data Analysis, 11(6):627–650, 2007.

[43] D. Dubois, E. Hullermeier,¨ and H. Prade. A systematic approach to the assessment of fuzzy association rules. Data Mining and Knowledge Discovery, 13(2):167–192, 2006.

[44]E.H ullermeier.¨ Fuzzy sets in machine learning and data mining: Status and prospects. Fuzzy Sets and Systems, 156(3):387–406, 2005.

[45] D. Dubois and E. Hullermeier.¨ Comparing probability measures using possibility theory: A notion of relative peakedness. International Journal of Approximate Reasoning, 45(2):364–385, 2007.

IV/XIX [46] D. Dubois, E. Hullermeier,¨ and H. Prade. Fuzzy methods in case-based recommendation and decision support. Journal of Intelligent Information Systems, 27:95–115, 2006. [47]E.H ullermeier¨ and J. Beringer. Learning from ambiguously labeled examples. Intelligent Data Analysis, 10(5):419–440, 2006. [48]E.H ullermeier.¨ Experience-based decision making: A satisficing decision tree approach. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 35(5):641–653, 2005. [49] N. Weskamp, E. Hullermeier,¨ D. Kuhn, and G. Klebe. Multiple graph alignment for the structural analysis of protein active sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(2):310–320, 2007. [50] P. Block, J. Paern, E. Hullermeier,¨ P. Sanschagrin, C. Sotriffer, and G. Klebe. Physicochemical descriptors to discriminate protein interactions in permanent and transient complexes by means of machine learning algorithms. Proteins: Structure, Function, and Bioinformatics, 65(3):607–622, 2006. [51] R. Balasubramaniyan, E. Hullermeier,¨ N. Weskamp, and J. Kamper.¨ Clustering of gene expression data using a local shape-based similarity measure. Bioinformatics, 21(7):1069–1077, 2005. [52] D. Kuhn, N. Weskamp, S. Schmitt, E. Hullermeier,¨ and G. Klebe. From the similarity analysis of protein cavities to the functional classification of protein families using Cavbase. Journal of Molecular Biology, 359(4):1023–1044, 2006. [53] J. Beringer and E. Hullermeier.¨ Online clustering of parallel data streams. Data and Knowledge Engineering, 58(2):180–204, 2006. [54] J. Beringer and E. Hullermeier.¨ Case-based learning in a bipolar possibilistic framework. International Journal of Intelligent Systems, 23(10):1119–1134, 2008. [55]E.H ullermeier,¨ I. Renners, and A. Grauel. An evolutionary approach to constraint-regularized learning. Mathware and Soft Computing, 11(2–3):109–124, 2004. [56] N. Weskamp, D. Kuhn, E. Hullermeier,¨ and G. Klebe. Efficient similarity search in protein structure databases: Improving clique-detection through clique-hashing. Bioinformatics, 20(10):1522–1526, 2004. [57]E.H ullermeier.¨ Flexible constraints for regularization in learning from data. International Journal of Intelligent Systems, 19(6):525–541, 2004. [58]E.H ullermeier.¨ Possibilistic instance-based learning. Artificial Intelligence, 148(1–2):335–383, 2003. [59] M. de Calmes,` D. Dubois, E. Hullermeier,¨ H. Prade, and F. Sedes.` Flexibility and case-based evaluation in querying: An illustration in an experimental setting. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(1):43–66, 2003. [60] D. Dubois, E. Hullermeier,¨ and H. Prade. On the representation of fuzzy rules in terms of crisp rules. Information Sciences, 151:301–326, 2003. [61]E.H ullermeier,¨ D. Dubois, and H. Prade. Model adaptation in possibilistic instance-based reasoning. IEEE Transactions on Fuzzy Systems, 10(3):333–339, 2002. [62] D. Dubois, E. Hullermeier,¨ and H. Prade. Fuzzy set-based methods in instance-based reasoning. IEEE Transactions on Fuzzy Systems, 10(3):322–332, 2002. [63]E.H ullermeier.¨ Similarity-based inference as evidential reasoning. International Journal of Approximate Reasoning, 26:67–100, 2001.

V/XIX [64] I. Karimi and E. Hullermeier.¨ Risk assessment for natural hazards: A new approach based on fuzzy probability. Fuzzy Sets and Systems, 158(9):987–999, 2007. [65] I. Karimi, E. Hullermeier,¨ and K. Meskouris. A fuzzy-probabilistic earthquake risk assessment system. Soft Computing, 11(3):229–238, 2006. [66]E.H ullermeier¨ and C. Giersch. Fuzzy dynamics and applications in biological systems modelling. Systems Analysis, Modelling, Simulation, 38:29–50, 2000. [67]E.H ullermeier,¨ M. Kraft, and P. Weise. Evaluation and specification of a synergetic business cycle model with German data. Homo Oeconomicus, XVI, 2000. [68]E.H ullermeier.¨ Numerical methods for fuzzy initial value problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 7(5):439–461, 1999. [69]E.H ullermeier.¨ Approximation of uncertain functional relationships. Fuzzy Sets and Systems, 101(2):227–240, January 1999. [70]E.H ullermeier.¨ A new approach to modelling and simulation of uncertain dynamical systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 5(2):117–137, April 1997.

4 CHAPTERS IN BOOKS

[1]E.H ullermeier¨ and A. Fallah Tehrani. Efficient learning of classifiers based on the 2-additive Choquet integral. In C. Moewes and A. Nurnberger,¨ editors, Computational Intelligence in Intelligent Data Analysis, Studies in Computational Intelligence, pages 17–30. Springer, 2012. [2]E.H ullermeier.¨ Fuzzy rules in data mining: From fuzzy associations to gradual dependencies. In E. Trillas, P.P. Bonissone, L. Magdalena, and J. Kacprzyk, editors, Combining Experimentation and Theory, volume 271 of Studies in Fuzziness and Soft Computing, pages 123–135. Springer, 2012. [3] J. Huhn¨ and E. Hullermeier.¨ An analysis of the FURIA algorithm for fuzzy rule induction. In J. Koronacki, Z.W. Ras, S.T. Wierzchon, and J. Kacprzyk, editors, Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski, number 262 in Studies in Computational Intelligence, pages 321–344. Springer-Verlag, 2010. [4]E.H ullermeier.¨ On the usefulness of fuzzy sets in data mining. In R. Seising, editor, Views on Fuzzy Sets and Systems from Different Perspectives: Philosophy and Logic, Criticisms and Applications, pages 457–470. Springer-Verlag, 2009. [5]E.H ullermeier.¨ Fuzzy methods in data mining. In J. Wang, editor, Encyclopedia of Data Warehousing and Mining – Second Edition, pages 907–912. Idea Group, Inc., Hershey, USA, 2008. [6]E.H ullermeier¨ and J. Furnkranz.¨ Learning preference models from data: On the problem of label ranking and its variants. In G. Della Riccia, D. Dubois, R. Kruse, and H.J. Lenz, editors, Preferences and Similarities, pages 283–304. Springer-Verlag, 2008. [7]E.H ullermeier.¨ Why fuzzy set theory is useful in data mining. In F. Masseglia, P. Poncelet, and M. Teisseire, editors, Successes and New Directions in Data Mining, pages 1–16. Information Science Reference, Hershey, New York, 2007. [8]E.H ullermeier.¨ Granular computing in machine learning and data mining. In W. Pedrycz, A. Skowron, and V. Kreinovich, editors, Handbook on Granular Computing, pages 889–906. John Wiley and Sons, 2008.

VI/XIX [9] J. Beringer and E. Hullermeier.¨ Fuzzy clustering of parallel data streams. In J. Valente de Oliveira and W. Pedrycz, editors, Advances in Fuzzy Clustering and Its Applications, pages 333–352. John Wiley and Sons, 2007.

[10]E.H ullermeier.¨ Fuzzy methods for data mining and machine learning: State of the art and prospects. In H. Bustince, F. Herrera, and J. Montero, editors, Fuzzy Sets and their Extensions: Representation, Aggregation and Models, pages 355–374. Springer-Verlag, 2007.

[11]E.H ullermeier.¨ Sequential decision making in heuristic search. In G. Della Riccia, D. Dubois, R. Kruse, and H.J. Lenz, editors, Planning Based on Decision Theory (CISM Courses and Lectures No. 472). Springer-Verlag, 2003.

[12] D. Dubois, E. Hullermeier,¨ and H. Prade. Possibilistic case-based decisions. In C. Lesage and M. Cottrell, editors, Connectionist Approaches in Economics and Management Sciences, number 6 in Advances in Computational Management Sciences. Kluwer, 2003.

[13]E.H ullermeier¨ and J. Beringer. Mining implication-based fuzzy association rules in databases. In B. Bouchon-Meunier, L. Foulloy, and R.R. Yager, editors, Intelligent Systems for Information Processing: From Representation to Applications. Elsevier, 2003.

[14]E.H ullermeier.¨ Maschinelles Lernen und Komplexitat.¨ In F. Haslinger and P. Weise, editors, Okonomie¨ und Gesellschaft, Jahrbuch 17: Komplexitat¨ und Lernen, pages 255–288. Metropolis-Verlag, 2001.

[15]E.H ullermeier,¨ D. Dubois, and H. Prade. Knowledge-based extrapolation of cases: A possibilistic approach. In B. Bouchon-Meunier, J. Gutierrez-Rios, L. Magdalena, and R.R. Yager, editors, Technologies for Constructing Intelligent Systems, pages 377–390. Springer-Verlag, 2002.

[16] M. Kraft, E. Hullermeier,¨ and P. Weise. Empirische Uberpr¨ ufung¨ eines synergetischen Konjunkturmodells. In H.-W. Lorenz and B. Meyer, editors, Studien zur Evolutorischen Okonomik¨ IV, pages 11–44. Duncker & Humblot, Berlin, 2001.

[17] D. Dubois, E. Hullermeier,¨ and H. Prade. Formalizing case-based inference using fuzzy rules. In S.K. Pal, D.Y. So, and T. Dillon, editors, Soft Computing in Case-Based Reasoning, pages 47–72. Springer-Verlag, 2000.

[18]E.H ullermeier.¨ Qualitatives Schließen und Qualitative Simulation. In H. Szczerbicka and T. Uthmann, editors, Modellierung, Simulation und Kunstliche¨ Intelligenz, pages 277–310. SCS Publishing House, Erlangen, 1999.

[19]E.H ullermeier,¨ M. Kraft, and P. Weise. Konjunkturzyklen aufgrund von Investitionsinterdependenzen: Simulation und empirische Uberpr¨ ufung.¨ In J. Flemmig, editor, Moderne Makrookonomik¨ – Eine kritische Bestandsaufnahme, pages 413–454. Metropolis-Verlag, 1995.

5 INTERNATIONAL CONFERENCES

5.1 Artificial Intelligence, Machine Learning and Data Mining

[1] G. Szarvas, R. Busa-Fekete, and E. Hullermeier.¨ Learning to rank lexical substitutions. In Proceedings EMNLP–2013, Conference on Empirical Methods in Natural Language Processing, Seattle, USA, 2013.

VII/XIX [2] K. Dembczynski, A. Jachnik, W. Kotlowski, W. Waegeman, and E. Hullermeier.¨ Optimizing the F-measure in multi-label classification: Plug-in rule approach versus structured loss minimization. In S. Dasgupta and D. McAllester, editors, Proceedings ICML–2013, 30th International Conference on Machine Learning, JMLR W& CP, volume 28, pages 1130–1138, Atlanta, USA, 2013. [24% acceptance rate].

[3] R. Busa-Fekete, B. Szoreny, P. Weng W. Cheng, and E. Hullermeier.¨ Top-k selection based on adaptive sampling of noisy preferences. In S. Dasgupta and D. McAllester, editors, Proceedings ICML–2013, 30th International Conference on Machine Learning, JMLR W& CP, volume 28, pages 1094–1102, Atlanta, USA, 2013. [24% acceptance rate].

[4]E.H ullermeier¨ and W. Cheng. Preference-based CBR: General ideas and basic principles. pages 3012–3016, Beijing, China, 2013. AAAI Press.

[5] S. Henzgen, M. Strickert, and E. Hullermeier.¨ Rule chains for visualizing evolving fuzzy rule-based systems. In R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, and A. Zolnierek, editors, Proceedings CORES 2013, 8th INternational Conference on Computer Recognition Systems, pages 279–288, Wroclaw, Poland, 2013. Springer-Verlag.

[6] A. Shaker and E. Hullermeier.¨ Recovery analysis for adaptive learning from non-stationary data streams. In R. Burduk, K. Jackowski, M. Kurzynski, M. Wozniak, and A. Zolnierek, editors, Proceedings CORES 2013, 8th International Conference on Computer Recognition Systems, pages 289–298, Wroclaw, Poland, 2013. Springer-Verlag.

[7] W. Cheng, E. Hullermeier,¨ W. Waegeman, and V. Welker. Label ranking with partial abstention based on thresholded probabilistic models. In Proceedings NIPS–2012, 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, US, 2012. [25% acceptance rate].

[8] W. Cheng and E. Hullermeier.¨ Probability estimation for multi-class classification based on label ranking. In Proceedings ECML/PKDD–2012, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Bristol, UK, 2012. [24% acceptance rate].

[9] K. Dembczynski, W. Kotlowski, and E. Hullermeier.¨ Consistent multilabel ranking through univariate loss minimization. In J. Langford and J. Pineau, editors, Proceedings ICML–2012, International Conference on Machine Learning, Edinburgh, Scotland, 2012. [27% acceptance rate].

[10] K. Dembczynski, W. Waegeman, and E. Hullermeier.¨ An analysis of chaining in multi-label classification. In Proceedings ECAI–2012, 20th European Conference on Artificial Intelligence, pages 294–299, Montpellier, France, 2012. IOS Press. [28% acceptance rate]. Best paper Award. [11] K. Dembczynski, W. Waegeman, W. Cheng, and E. Hullermeier.¨ An exact algorithm for F-measure maximization. In Proceedings NIPS–2011, 25th Annual Conference on Neural Information Processing Systems, Granada, Spain, 2011. [21% acceptance rate].

[12] W. Cheng, J. Furnkranz,¨ E. Hullermeier,¨ and S.H. Park. Preference-based policy iteration: Leveraging preference learning for reinforcement learning. In Proceedings ECML/PKDD–2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece, 2011. [20% acceptance rate]. Selected as one of the 10 best ECML papers. [13] J. Furnkranz¨ and E. Hullermeier.¨ Learning from label preferences. In T. Elomaa, J. Hollmen, and H. Mannila, editors, Proceedings DS–2011, 14th International Conference on Discovery Science, number 6926 in LNAI, pages 2–17. Springer-Verlag, 2011.

VIII/XIX [14] A. Fallah Tehrani, W. Cheng, K. Dembczynski, and E. Hullermeier.¨ Learning monotone nonlinear models using the Choquet integral. In Proceedings ECML/PKDD–2011, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Athens, Greece, 2011. [20% acceptance rate]. Selected as one of the 10 best ECML papers. [15]E.H ullermeier¨ and P. Schlegel. Preference-based CBR: First steps toward a methodological framework. In A. Ram and N. Wiratunga, editors, Proceedings ICCBR–2011, 19th International Conference on Case-Based Reasoning, number 6880 in LNAI, pages 77–91. Springer-Verlag, 2011. [53% acceptance rate]. Best Paper Award. [16] W. Kotlowski, K. Dembczynski, and E. Hullermeier.¨ Bipartite ranking through minimization of univariate loss. In Proceedings ICML–2011, 28th International Conference on Machine Learning, Washington, USA, 2011. [25% acceptance rate].

[17] K. Dembczynski, W. Cheng, and E. Hullermeier.¨ Bayes optimal multilabel classification via probabilistic classifier chains. In J. Furnkranz¨ and T. Joachims, editors, Proceedings ICML–2010, International Conference on Machine Learning, Haifa, Israel, 2010. [25% acceptance rate].

[18] W. Cheng, K. Dembczynski, and E. Hullermeier.¨ Label ranking based on the Plackett-Luce model. In J. Furnkranz¨ and T. Joachims, editors, Proceedings ICML–2010, International Conference on Machine Learning, Haifa, Israel, 2010. [25% acceptance rate].

[19] W. Cheng, K. Dembczynski, and E. Hullermeier.¨ Graded multi-label classification: The ordinal case. In J. Furnkranz¨ and T. Joachims, editors, Proceedings ICML–2010, International Conference on Machine Learning, Haifa, Israel, 2010. [25% acceptance rate].

[20] K. Dembczynski, W. Waegeman, W. Cheng, and E. Hullermeier.¨ Regret analysis for performance metrics in multi-label classification: The case of Hamming and subset zero-one loss. In Proceedings ECML/PKDD–2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Barcelona, Spain, 2010. [18% acceptance rate].

[21] W. Cheng, M. Rademaker, B. De Beats, and E. Hullermeier.¨ Predicting partial orders: Ranking with abstention. In Proceedings ECML/PKDD–2010, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Barcelona, Spain, 2010. [18% acceptance rate].

[22] W. Cheng, J. Huhn,¨ and E. Hullermeier.¨ Decision tree and instance-based learning for label ranking. In Proceedings ICML–2009, 26th International Conference on Machine Learning, Montreal, Canada, 2009. [27% acceptance rate].

[23] J. Furnkranz,¨ E. Hullermeier,¨ and S. Vanderlooy. Binary decomposition methods for multipartite ranking. In Proceedings ECML/PKDD–2009, European Conference on Machine Learning and Knowledge Discovery in Databases, Bled, Slovenia, 2009. [24% acceptance rate].

[24] W. Cheng and E. Hullermeier.¨ A new instance-based label ranking approach using the Mallows model. In Advances in Neural Networks (Proceedings 6th International Symposium on Neural Networks), LNCS 5551, pages 707–716, Wuhan, China, 2009. Springer-Verlag. [33% acceptance rate].

[25]E.H ullermeier,¨ I. Vladimirskiy, B. Prados Suarez, and E. Stauch. Supporting case-based retrieval by similarity skylines: Basic concepts and extensions. In K.D. Althoff, R. Bergmann, M. Minor, and A. Hanft, editors, Proceedings ECCBR–2008, 9th European Conference on Case-Based Reasoning, number 5239 in LNAI, pages 240–254, Trier, Germany, 2008. Springer-Verlag. [25% acceptance rate (for oral presentation)]. Best Paper Award.

IX/XIX [26] W. Cheng and E. Hullermeier.¨ Learning similarity functions from qualitative feedback. In K.D. Althoff, R. Bergmann, M. Minor, and A. Hanft, editors, Proceedings ECCBR–2008, 9th European Conference on Case-Based Reasoning, number 5239 in LNAI, pages 120–134, Trier, Germany, 2008. Springer-Verlag. [25% acceptance rate (for oral presentation)]. [27] J.N. Sulzmann, J. Furnkranz,¨ and E. Hullermeier.¨ On pairwise Naive Bayes classifiers. In Proceedings ECML–07, 17th European Conference on Machine Learning, Warsaw, Poland, September 2007. Springer-Verlag. [12% acceptance rate]. [28]E.H ullermeier¨ and J. Furnkranz.¨ On minimizing the position error in label ranking. In Proceedings ECML–07, 17th European Conference on Machine Learning, Warsaw, Poland, September 2007. Springer-Verlag. [24% acceptance rate]. [29] K. Brinker and E. Hullermeier.¨ Label ranking in case-based reasoning. In M. Richter and R. Weber, editors, Proceedings ICCBR–2007, 7th International Conference on Case-Based Reasoning, number 4626 in LNAI, pages 77–91, Belfast, Northern Ireland, 2007. Springer-Verlag. [36% acceptance rate]. [30] J. Beringer and E. Hullermeier.¨ Efficient instance-based learning on data streams. In P. Perner, editor, Proceedings ICDM–2007, International Conference on Industrial Data Mining, pages 34–48, Leipzig, Germany, 2007. Springer-Verlag. [26% acceptance rate]. [31] K. Brinker and E. Hullermeier.¨ Case-based multilabel ranking. In Proc. IJCAI–07, 20th International Joint Conference on Artificial Intelligence, pages 701–707, Hyderabad, India, January 2007. [15% acceptance rate]. [32] K. Bade, E. Hullermeier,¨ and A. Nurnberger.¨ Hierarchical classification by expected utility maximization. In Proceedings ICDM 2006, IEEE International Conference on Data Mining, pages 43–52. IEEE Computer Society Press, 2006. [11% acceptance rate]. [33] K. Brinker and E. Hullermeier.¨ Case-based label ranking. In Proceedings ECML–06, 17th European Conference on Machine Learning, pages 566–573, Berlin, September 2006. Springer-Verlag. [24% acceptance rate]. [34] K. Brinker, J. Furnkranz,¨ and E. Hullermeier.¨ A unified model for multilabel classification and ranking. In G. Brewka, S. Coradeschi, A. Perini, and P. Traverso, editors, Proceedings ECAI–2006, 17th European Conference on Artificial Intelligence, pages 489–493, Riva del Garda, Italy, 2006. [26% acceptance rate]. [35]E.H ullermeier.¨ Cho-k-NN: A method for combining interacting pieces of evidence in case-based learning. In L. Kaelbling and A. Saffiotti, editors, Proceedings IJCAI–05, 19th International Joint Conference on Artificial Intelligence, pages 3–8, Edinburgh, Scotland, 2005. [18% acceptance rate]. [36]E.H ullermeier¨ and J. Furnkranz.¨ Learning label preferences: Ranking error versus position error. In A. Famili, J. Kok, J. Pena, A. Siebes, and A. Feelders, editors, Proceedings IDA–05, 6th International Symposium on Intelligent Data Analysis, number 3646 in Lecture Notes in Computer Sciences, pages 180–191, Madrid, 2005. Springer-Verlag. [25% acceptance rate]. [37]E.H ullermeier¨ and J. Beringer. Learning from ambiguously labeled examples. In A. Famili, J. Kok, J. Pena, A. Siebes, and A. Feelders, editors, Proceedings IDA–05, 6th International Symposium on Intelligent Data Analysis, number 3646 in LNAI, pages 168–179, Madrid, 2005. Springer-Verlag. [25% acceptance rate]. [38]E.H ullermeier.¨ Instance-based prediction with guaranteed confidence. In R. Lopez de Mantaras and L. Saitta, editors, Proceedings ECAI–2004, 16th European Conference on Artificial Intelligence, pages 97–101, Valencia, Spain, 2004. IOS Press. [27% acceptance rate].

X/XIX [39]E.H ullermeier.¨ On the representation and combination of evidence in instance-based learning. In Proceedings ECAI–2002, 15th European Conference on Artificial Intelligence, pages 360–364, Lyon, France, 2002. IOS Press. [27% acceptance rate]. Runner Up Paper Award. [40]E.H ullermeier.¨ Similarity-based inference as evidential reasoning. In W. Horn, editor, Proceedings ECAI–2000, 14th European Conference on Artificial Intelligence, pages 50–54, Berlin, Germany, 2000. IOS Press. [31% acceptance rate].

[41]E.H ullermeier.¨ Focusing search by using problem solving experience. In W. Horn, editor, Proceedings ECAI–2000, 14th European Conference on Artificial Intelligence, pages 55–59, Berlin, Germany, 2000. IOS Press. [31% acceptance rate].

[42]E.H ullermeier.¨ Toward a probabilistic formalization of case-based inference. In T. Dean, editor, Proceedings IJCAI–99, 16th International Joint Conference on Artificial Intelligence, pages 248–253, Stockholm, Sweden, July/August 1999. Morgan Kaufmann. [26% acceptance rate].

[43]E.H ullermeier.¨ Change detection in heuristic search. In Proceedings AAAI–2000, 17th National Conference on Artificial Intelligence, pages 898–903, Austin, Texas, July 2000. [33% acceptance rate].

[44] M. de Calmes,` D. Dubois, E. Hullermeier,¨ H. Prade, and F. Sedes.` A fuzzy set approach to flexible case-based querying: methodology and experimentation. In D. Fensel, F. Giunchiglia, D. McGuinness, and M.A. Williams, editors, Proceedings KR–02, 8th International Conference on Principles of Knowledge Representation and Reasoning, pages 449–458, Toulouse, France, April 2002. Morgan Kaufmann Publishers. [32% acceptance rate].

[45]E.H ullermeier.¨ Regularized learning with flexible constraints. In M.R. Berthold, H.J. Lenz, E. Bradley, R. Kruse, and C. Borgelt, editors, Proceedings IDA–03, 5th International Symposium on Intelligent Data Analysis, number 2810 in Lecture Notes in Computer Science, Berlin, August 2003. Springer-Verlag. [33% acceptance rate].

[46] J. Furnkranz¨ and E. Hullermeier.¨ Pairwise preference learning and ranking. In Proceedings ECML–03, 13th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 2003. Springer-Verlag. [24% acceptance rate].

[47]E.H ullermeier.¨ Association rules for expressing gradual dependencies. In Proceedings PKDD–02, 6th European Conference on Principles and Practice of Knowledge Discovery in Databases, pages 200–211, Helsinki, Finland, August 2002. Springer-Verlag. [37% acceptance rate].

[48]E.H ullermeier.¨ Possibilistic induction in decision tree learning. In Proceedings ECML–02, 13th European Conference on Machine Learning, pages 173–184, Helsinki, Finland, August 2002. Springer-Verlag. [37% acceptance rate].

[49] D. Dubois and E. Hullermeier.¨ A notion of comparative probabilistic entropy based on the possibilistic specificity ordering. In ECSQARU-2005, 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Barcelona, 2005. [60% acceptance rate].

[50]E.H ullermeier.¨ Implication-based fuzzy association rules. In L. De Raedt and A. Siebes, editors, Proceedings PKDD–01, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, number 2168 in LNAI, pages 241–252, Freiburg, Germany, September 2001. Springer-Verlag. [37% acceptance rate].

XI/XIX [51]E.H ullermeier.¨ Exploiting similarity for supporting data analysis and problem solving. In D.J. Hand, J.N. Kok, and M.R. Berthold, editors, Advances in Intelligent Data Analysis, Proceedings of the 3rd International Symposium, IDA–99, number 1642 in Lecture Notes in Computer Science, pages 257–268, Amsterdam, August 1999. Springer-Verlag.

[52]E.H ullermeier.¨ A method for predicting solutions in case-based problem solving. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning, Proceedings EWCBR–2000, 5th European Workshop on Case-Based Reasoning, pages 124–135, Trento, Italy, 2000. Springer-Verlag.

[53] D. Dubois, E. Hullermeier,¨ and H. Prade. Flexible control of case-based prediction in the framework of possiblity theory. In E. Blanzieri and L. Portinale, editors, Advances in Case-Based Reasoning, Proceedings EWCBR–2000, 5th European Workshop on Case-Based Reasoning, pages 61–73, Trento, Italy, 2000. Springer-Verlag.

5.2 Computational Intelligence, Fuzzy Set Theory

[1] A. Fallah Tehrani and E. Hullermeier.¨ Ordinal Choquistic regression. In J. Montero, G. Pasi, and D. Ciucci, editors, Proceedings EUSFLAT–2013, 8th International Conference of the European Society for Fuzzy Logic and Technology, Milano, Italy, 2013. Atlantis Press.

[2] M. Nasiri, T. Fober, R. Senge, and E. Hullermeier.¨ Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing. In Proceedings IFSA–2013, World Congress of the International Fuzzy Systems Association, pages 715–721, Edmonton, Canada, 2013.

[3]E.H ullermeier¨ and A. Fallah Tehrani. On the VC dimension of the Choquet integral. In IPMU–2012, 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Catania, Italy, 2012.

[4] M. Nasiri, E. Hullermeier,¨ R. Senge, and E. Lughofer. Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry. In Proceedings IFSA–2011, World Congress of the International Fuzzy Systems Association, pages RW–103–1–6, Surabaya and Bali Island, Indonesia, 2011.

[5] E. Lughofer and E. Hullermeier.¨ On-line redundancy deletion in evolving fuzzy regression models using a fuzzy inclusion measure. In S. Galichet, J. Montero, and G. Mauris, editors, Proceedings EUSFLAT–2011, 7th International Conference of the European Society for Fuzzy Logic and Technology, pages 380–387, Aix-les-Bains, France, 2011.

[6] A. Fallah Tehrani, W. Cheng, and E. Hullermeier.¨ Choquistic regression: Generalizing logistic regression using the Choquet integral. In S. Galichet, J. Montero, and G. Mauris, editors, Proceedings EUSFLAT–2011, 7th International Conference of the European Society for Fuzzy Logic and Technology, pages 868–875, Aix-les-Bains, France, 2011.

[7] T. Fober and E. Hullermeier.¨ Similarity measures for protein structures based on fuzzy histogram comparison. In Proceedings WCCI–2010, World Congress on Computational Intelligence, Barcelona, Spain, 2010.

[8] R. Senge and E. Hullermeier.¨ Pattern trees for regression and fuzzy systems modeling. In Proceedings WCCI–2010, World Congress on Computational Intelligence, Barcelona, Spain, 2010.

[9] T. Fober, G. Klebe, and E. Hullermeier.¨ Efficient construction of multiple geometrical alignments for the comparison of protein binding sites. In Proceedings ISDA–2009, 9th International Conference on Intelligent Systems Design and Applications, pages 1251–1256, Pisa, Italy, 2009.

XII/XIX [10] I. Boukhris, Z. Elouedi, T. Fober, M. Mernberger, and E. Hullermeier.¨ Similarity analysis of protein binding sites: A generalization of the maximum common subgraph measure based on quasi-clique detection. In Proceedings ISDA–2009, 9th International Conference on Intelligent Systems Design and Applications, pages 1245–1250, Pisa, Italy, 2009.

[11] T. Fober and E. Hullermeier.¨ Fuzzy modeling of labeled point cloud superposition for the comparison of protein binding sites. In Proceedings IFSA/EUSFLAT–2009, World Congress of the Fuzzy Systems Association, pages 1299–1304, Lisbon, Portugal, 2009.

[12]E.H ullermeier¨ and M. Rifqi. A fuzzy variant of the Rand index for comparing clustering structures. In Proceedings IFSA/EUSFLAT–2009, World Congress of the Fuzzy Systems Association, pages 1294–1298, Lisbon, Portugal, 2009.

[13]E.H ullermeier¨ and K. Brinker. Fuzzy-relational classification: Combining pairwise decomposition techniques with fuzzy preference modeling. In Proceedings EUSFLAT–2007, 5th International Conference of the European Society for Fuzzy Logic and Technology, volume 1, pages 353–360, Ostrava, Czech Republic, September 2007. Distinguished Paper Award. [14] J. Beringer and E. Hullermeier.¨ Adaptive optimization of the number of clusters in fuzzy clustering. In FUZZ-IEEE–07, IEEE International Conference on Fuzzy Systems, London, 2007.

[15]E.H ullermeier,¨ N. Weskamp, G. Klebe, and D. Kuhn. Graph alignment: Fuzzy pattern mining for the structural analysis of protein active sites. In FUZZ-IEEE–07, IEEE International Conference on Fuzzy Systems, London, 2007.

[16]E.H ullermeier.¨ Integrating instance-based learning and logistic regression. In Proceedings IPMU–06, 11th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Paris, 2006.

[17]E.H ullermeier.¨ The Choquet integral as an aggregation operator in case-based learning. In B. Reusch, editor, Proceedings of the International Conference on Computational Intelligence – 9th Fuzzy Days, pages 615–628, Dortmund, Germany, September 2006. Springer-Verlag.

[18] I. Karimi and E. Hullermeier.¨ A fuzzy-probablistic risk assessment system for natural disasters. In Proceedings IFSA–2005, 11th World Congress of the International Fuzzy Systems Association, pages 1147–1153, Beijing, China, July 2005. Best Student Paper Award. [19] E. Lughofer, E. Hullermeier,¨ and E. Klement. Improving the interpretability of data-driven evolving fuzzy systems. In Proceedings EUSFLAT–2005, 4th International Conference of the European Society for Fuzzy Logic and Technology, Barcelona, September 2005.

[20] Y. Yi and E. Hullermeier.¨ Learning complexity-bounded rule-based classifiers by combining association analysis and genetic algorithms. In Proceedings EUSFLAT–2005, 4th International Conference of the European Society for Fuzzy Logic and Technology, Barcelona, September 2005.

[21]E.H ullermeier¨ and J. Furnkranz.¨ Comparison of ranking procedures in pairwise preference learning. In IPMU–04, 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, Italy, 2004.

[22]E.H ullermeier¨ and J. Furnkranz.¨ Ranking by pairwise comparison: A note on risk minimization. In FUZZ-IEEE–04, IEEE International Conference on Fuzzy Systems, Budapest, Hungary, July 2004.

XIII/XIX [23] I. Karimi, E. Hullermeier,¨ and K. Meskouris. An earthquake risk assessment method based on fuzzy probability. In 6th International FLINS Conference on Applied Computational Intelligence, Duinse Polders, Blankenberge, Belgium, September 2004. [24]E.H ullermeier¨ and J. Beringer. Learning decision rules from positive and negative preferences. In IPMU–04, 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, Italy, 2004. [25]E.H ullermeier.¨ Instance-based collaborative filtering with fuzzy labels. In Proceedings EUSFLAT–2003, 3rd International Conference of the European Society for Fuzzy Logic and Technology, pages 468–473, Zittau, Germany, September 2003. [26] D. Dubois, E. Hullermeier,¨ and H. Prade. A note on quality measures for fuzzy association rules. In T. Bilgic, B. De Baets, and O. Kaynak, editors, Proceedings IFSA–03, 10th International Fuzzy Systems Association World Congress, number 2715 in Lecture Notes in Artificial Intelligence, pages 677–648, Istambul, July 2003. Springer-Verlag. [27]E.H ullermeier.¨ Inducing fuzzy concepts through extended version space learning. In T. Bilgic, B. De Baets, and O. Kaynak, editors, Proceedings IFSA–03, 10th International Fuzzy Systems Association World Congress, number 2715 in Lecture Notes in Artificial Intelligence, pages 677–648, Istambul, July 2003. Springer-Verlag. [28]E.H ullermeier.¨ Mining implication-based fuzzy association rules in databases. In Proceedings IPMU–02, 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 101–108, Annecy, France, July 2002. [29]E.H ullermeier.¨ Exploiting similarity and experience in decision making. In FUZZ-IEEE–02, IEEE International Conference on Fuzzy Systems, pages 729–734, Honolulu, Hawaii, May 2002. [30] M. de Calmes,` D. Dubois, E. Hullermeier,¨ H. Prade, and F. Sedes.` Case-based querying and prediction: A fuzzy set approach. In FUZZ-IEEE–02, IEEE International Conference on Fuzzy Systems, pages 735–740, Honolulu, Hawaii, May 2002. [31]E.H ullermeier.¨ Fuzzy association rules: Semantic issues and quality measures. In B. Reusch, editor, Proceedings of the International Conference on Computational Intelligence – 7th Fuzzy Days, number 2206 in LNCS, pages 380–391, Dortmund, Germany, 2001. Springer-Verlag. [32] D. Dubois, E. Hullermeier,¨ and H. Prade. Toward the representation of implication-based fuzzy rules in terms of crisp rules. In Proceedings IFSA/NAFIPS-2001, Joint 9th World Congress of the International Fuzzy Systems Association and 20th International Conference of the North American Fuzzy Information Processing Society, pages 1592–1597, Vancouver, Canada, July 2001. [33]E.H ullermeier,¨ D. Dubois, and H. Prade. Knowledge-based extrapolation of cases: A possibilistic approach. In Proceedings IPMU–2000, 8th International Confernce on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 1575–1582, Madrid, July 2000. [34]E.H ullermeier,¨ D. Dubois, and H. Prade. Extensions of a qualitative approach to case-based decision making: Uncertainty and fuzzy quantification in act evaluation. In H.J. Zimmermann, editor, EUFIT–99, 7th European Congress on Intelligent Techniques and Soft Computing, Aachen, September 1999. [35]E.H ullermeier.¨ Toward models of case-based decision making. In H.J. Zimmermann, editor, EUFIT–99, 7th European Congress on Intelligent Techniques and Soft Computing, Aachen, September 1999.

XIV/XIX [36]E.H ullermeier.¨ A possibilistic formalization of case-based reasoning and decision making. In B. Reusch, editor, Proceedings of the 6th International Conference on Computational Intelligence, number 1625 in Lecture Notes in Computer Science, pages 411–420, Dortmund, Germany, May 1999. Springer-Verlag.

[37]E.H ullermeier.¨ Fuzzy dynamics of compartmental systems. In H.J. Zimmermann, editor, EUFIT–98, 6th European Congress on Intelligent Techniques and Soft Computing, Aachen, September 1998.

[38]E.H ullermeier¨ and C. Giersch. Fuzzy sets and modelling of uncertainty in biological systems. In Proceedings IPMU–98, 7th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 1857–1864, Paris, La Sorbonne, July 1998. Editions E.D.K.

[39]E.H ullermeier.¨ A Bayesian approach to case-based probabilistic reasoning. In Proceedings IPMU–98, 7th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 1296–1303, Paris, La Sorbonne, July 1998. Editions E.D.K.

[40]E.H ullermeier.¨ Hierarchical propagation of uncertain constraints. In H.J. Zimmermann, editor, EUFIT–97, 5th European Congress on Intelligent Techniques and Soft Computing, pages 953–957, Aachen, September 1997.

[41]E.H ullermeier.¨ Approximation of fuzzy functions. In A. Grauel, W. Becker, and F. Belli, editors, Proceedings 4. Internationaler Workshop Fuzzy-Neuro-Systeme, pages 374–381, Soest, Germany, March 1997.

[42]E.H ullermeier.¨ Approximate probabilistic reasoning with fuzzy constraints. In H.J. Zimmermann, editor, EUFIT–96, 4th European Congress on Intelligent Techniques and Soft Computing, Aachen, September 1996.

[43]E.H ullermeier.¨ A fuzzy simulation method. In P.G. Anderson and K. Warwick, editors, International Symposium on Soft Computing, pages B230–B236, Reading, U.K., March 1996. ICSC Academic Press.

[44]E.H ullermeier.¨ Towards modelling of fuzzy functions. In H.J. Zimmermann, editor, EUFIT–95, 3rd European Congress on Intelligent Techniques and Soft Computing, pages 150–154, Aachen, September 1995.

[45]E.H ullermeier.¨ Rule-based modelling in fuzzy simulation. In N.C. Steele, editor, Proceedings ISFL–95, International Symposium on Fuzzy Logic, pages B49–B56, Zurich,¨ May 1995. ICSC Academic Press.

[46]E.H ullermeier.¨ Approximative solution of a linear programming problem using a modified perceptron-algorithm. In H.J. Zimmermann, editor, EUFIT–94, 2nd European Congress on Intelligent Techniques and Soft Computing, pages 195–199, Aachen, September 1994.

5.3 Other Conferences

[1] M. Leinweber, L. Baumgartner,¨ M. Mernberger, T. Fober, E. Hullermeier,¨ G. Klebe, and B. Freisleben. GPU-based cloud computing for comparing the structure of protein binding sites. In IEEE Conference on Digital Ecosystem Technologies—Complex Environment Engineering, Campione d’Italia, Italy, 2012.

[2]E.H ullermeier.¨ Experience-based decision making and learning from examples. In Proceedings SOR–01, Symposium on Operations Research, Duisburg, Germany, September 2001. Springer-Verlag.

XV/XIX [3]E.H ullermeier.¨ Fuzzy dynamics: Methodological framework and applications to compartmental modeling. In D.M. Dubois, editor, Proceedings CASYS–2000, 4th International Conference on Computing Anticipatory Systems, Liege,` Belgium, 2000. Best Paper Award. [4] I. Gueorguieva, I. Nestorov, M. Rowland, and E. Hullermeier.¨ In vitro–in vivo hepatic clearance prediction using fuzzy sets. In EUFEPS–2000, 6th European Congress of Pharmaceutical Sciences, Supplement 1 in European Journal of Pharmaceutical Sciences, Vol. 11, page S22, 2000. [5]E.H ullermeier.¨ Numerical solutions for fuzzy initial value problems. In P. Borne, M. Ksouri, and A. El Kamel, editors, CESA–98, IMACS Multiconference on Computational Engineering in Systems Applications, Symposium on Applied Mathematics and Optimization, Nabeul-Hammamet, Tunisia, April 1998. [6]E.H ullermeier.¨ Hierarchical constraint propagation based on interval arithmetic. In Proceedings Interval–96, International Conference on Interval Methods and Computer Aided Proofs in Science and Engineering, pages 54–55, Wurzburg,¨ October 1996. [7]E.H ullermeier.¨ Fuzzy polynomial and spline interpolation. In A. Sydow, editor, Proceedings of the IMACS Symposium on Systems Analysis and Simulation, pages 401–404. Gordon and Breach Publishers, Berlin, June 1995.

6 WORKSHOPS AND NATIONAL CONFERENCES

[1] R. Busa-Fekete, T. Fober, and E. Hullermeier.¨ Preference-based evolutionary optimization using generalized racing algorithms. In F. Hoffmann and E. Hullermeier,¨ editors, Proceedings 23. Workshop Computational Intelligence, pages 237–246, Dortmund, Germany, 2013. KIT Scientific Publishing. [2] S. Henzgen and E. Hullermeier.¨ Weighted rank correlation measures based on fuzzy order relations. In F. Hoffmann and E. Hullermeier,¨ editors, Proceedings 23. Workshop Computational Intelligence, pages 227–236, Dortmund, Germany, 2013. KIT Scientific Publishing. [3] P. Weng, R. Busa-Fekete, and E. Hullermeier.¨ Interactive Q-learning with ordinal rewards and unreliable tutor. In ECML/PKDD Workshop on Reinforcement learning from Generalized Feedback: Beyond Numerical Rewards. Prague, 2013. [4] R. Busa-Fekete, B. Szor¨ enyi,´ P. Weng, and E. Hullermeier.¨ Preference-based evolutionary direct policy search. In ECML/PKDD Workshop on Reinforcement learning from Generalized Feedback: Beyond Numerical Rewards. Prague, 2013. [5] W. Cheng and E. Hullermeier.¨ A nearest neighbor approach to label ranking based on generalized labelwise loss minimization. In Proceedings M-PREF’13, 7th Multidisciplinary Workshop on Advances in Preference Handling, Beijing, China, 2013. [6] A. Shaker and E. Hullermeier.¨ Event history analysis on data streams: An application to earthquake occurrence. In G. Krempl, I. Zliobaite, Y. Wang, and G. Forman, editors, Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, pages 38–41, Prague, Czech Republic, 2013. [7] R. Senge, J.J. del Coz, and E. Hullermeier.¨ On the problem of error propagation in classier chains for multi-label classification. In L. Schmidt-Thieme and M. Spiliopoulou, editors, Data Analysis, Machine Learning and Knowledge Discovery. Proceedings of GFKL–2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, Hildesheim, Germany, 2013.

XVI/XIX [8] K. Dembczynski, W. Waegeman, and E. Hullermeier.¨ Joint mode estimation in multi-label classification by chaining. In ECML Workshop on Collective Inference and Learning on Structured Data. Athens, Greece, 2011.

[9] T. Fober, G. Klebe, and E. Hullermeier.¨ Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics. In B. Lausen, D.Van˜ den Poel, and A. Ultsch, editors, Algorithms from and for Nature and Life (Proceedings GFKL–2011, Conference of the German Classification Society), pages 279–286. Springer-Verlag, Frankfurt, Germany, 2013.

[10] M. Mernberger, D. Moog, S. Stork, S. Zauner, U. Maier, and E. Hullermeier.¨ Prediction of protein localization for specialized compartments using time series kernels. In Proceedings GCB–2011, German Conference on Bioinformatics, Munich, Germany, 2011.

[11] T. Fober, M. Mernberger, G. Klebe, and E. Hullermeier.¨ Efficient similarity retrieval for protein binding sites based on histogram comparison. In Proceedings GCB–2010, German Conference on Bioinformatics, pages 51–60, Braunschweig, Germany, 2010.

[12] K. Dembczynski, W. Waegeman, W. Cheng, and E. Hullermeier.¨ On label dependence in multi-label classification. In Proceedings MLD–2010, 2nd International Workshop on Learning from Multi-Label Data, Haifa, Israel, 2010.

[13] W. Cheng and E. Hullermeier.¨ A simple instance-based approach to multilabel classification using the Mallows model. In Proceedings MLD–2009, International Workshop on Learning from Multi-Label Data, pages 28–38, Bled, Slovenia, 2009.

[14] R. Senge and E. Hullermeier.¨ Learning pattern tree classifiers using a co-evolutionary algorithm. In F. Hoffmann and E. Hullermeier,¨ editors, Proceedings 19. Workshop Computational Intelligence, pages 22–33, Dortmund, Germany, 2009. KIT Scientific Publishing.

[15] T. Fober, M. Mernberger, R. Moritz, and E. Hullermeier.¨ Graph-kernels for the comparative analysis of protein active sites. In I. Grosse, S. Neumann, S. Posch, F. Schreiber, and P. Stadler, editors, Proceedings GCB–2009, German Conference on Bioinformatics, pages 21–31, Halle (Saale), Germany, 2009. [38% acceptance rate].

[16] R. Senge and E. Hullermeier.¨ Learning pattern tree classifiers using a co-evolutionary algorithm. In Proceedings Workshop LWA–2009, Lernen–Wissensentdeckung–Adaptivitt, pages 105–110, Darmstadt, Germany, 2009.

[17] T. Fober, M. Mernberger, V. Melnikov, R. Moritz, and E. Hullermeier.¨ Extension and empirical comparison of graph-kernels for the analysis of protein active sites. In Proceedings Workshop LWA–2009, Lernen–Wissensentdeckung–Adaptivitt, pages 30–36, Darmstadt, Germany, 2009.

[18]E.H ullermeier¨ and S. Vanderlooy. Weighted voting as approximate MAP prediction in pairwise classification. In Proceedings Workshop LWA–2008, Lernen–Wissensentdeckung–Adaptivitat¨ , pages 34–41, Wurzburg,¨ Germany, 2008. [19] T. Fober, E. Hullermeier,¨ and M. Mernberger. Evolutionary construction of multiple graph alignments for mining structured biomolecular data. In Proceedings Workshop LWA–2008, Lernen–Wissensentdeckung–Adaptivitt, pages 27–33, Wurzburg,¨ Germany, 2008. [20] T. Fober, E. Hullermeier,¨ and M. Mernberger. Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. In A. Beyer and M. Schroeder, editors, Proceedings GCB–2008, German Conference on Bioinformatics, pages 44–53, Dresden, September 2008. [30% acceptance rate].

XVII/XIX [21] W. Cheng, E. Hullermeier,¨ B. Seeger, and I. Vladimirskiy. Interactive ranking of skylines using machine learning techniques. In Proceedings LWA-2007, Workshop Lernen–Wissen–Adaption, pages 141–148, Halle/Saale, Germany, 2007.

[22] T. Fober, E. Hullermeier,¨ and M. Mernberger. Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. In R. Mikut and M. Reischl, editors, Proceedings 17th Workshop Computational Intelligence, pages 1–14, Dortmund, Germany, 2007.

[23] I. Vladimirskiy, E. Hullermeier,¨ and E. Stauch. Similarity search over uncertain archaeological data using a modified skyline operator. In D.C. Wilson and D. Khemani, editors, Workshop Proceedings of ICCBR–07, 7th International Conference on Case-Based Reasoning, pages 31–40, Belfast, Northern Ireland, 2007.

[24] J. Beringer and E. Hullermeier.¨ Adaptive optimization of the number of clusters in fuzzy clustering. In R. Mikut and M. Reischl, editors, Proceedings 16th Workshop Computational Intelligence, pages 140–149, Dortmund, Germany, 2006.

[25]E.H ullermeier¨ and K. Brinker. Classification via fuzzy preference learning. In R. Mikut and M. Reischl, editors, Proceedings 16th Workshop Computational Intelligence, pages 190–199, Dortmund, Germany, 2006.

[26]E.H ullermeier.¨ Credible case-based inference using similarity profiles. In M. Minor, editor, Proceedings of ECCBR-06-Workshops, 8th European Conference on Case-Based Reasoning, pages 230–242, Ol¨ udeniz/Fethiye,¨ Turkey, 2006. [27] K. Brinker and E. Hullermeier.¨ Calibrated label-ranking. In S. Agarwal, C. Cortes, and R. Herbrich, editors, Proceedings of the NIPS-2005 Workshop on Learning to Rank, pages 1–6. Whistler, BC, Canada, 2005. Published electronically at http://web.mit.edu/shivani/www/Ranking-NIPS-05/.

[28]E.H ullermeier,¨ J. Furnkranz,¨ and J. Beringer. On position error and label ranking through iterated choice. In Proceedings LWA/FGML–2005, German Workshop on Machine Learning, pages 158–163, Saarbrucken,¨ Germany, October 2005. [29] D. Dubois, E. Hullermeier,¨ and H. Prade. A systematic approach to the assessment of fuzzy association rules. In JC. Cubero, D. Sanchez, Z. Ras, and T. Sudkamp, editors, Proceedings of the Workshop on Alternative Techniques for Data Mining and Knowledge Discovery, IEEE International Conference on Data Mining, Brighton, UK, November 2004.

[30] N. Weskamp, D. Kuhn, E. Hullermeier,¨ and G. Klebe. Efficient similarity search in protein structure databases: Improving clique-detection through geometric hashing. In Proceedings GCB–2003, German Conference on Bioinformatics, Munich, October 2003. [37% acceptance rate].

[31]E.H ullermeier.¨ Instance-based learning of credible label sets. In A. Gunter,¨ R. Kruse, and B. Neumann, editors, Proceedings KI–03, 26th German Conference on Artificial Intelligence, number 2821 in Lecture Notes in Artificial Intelligence, Hamburg, September 2003. Springer-Verlag. [46% acceptance rate].

[32] N. Weskamp, E. Hullermeier,¨ D. Kuhn, and G. Klebe. Graph alignments: A new concept to detect conserved regions in protein active sites. In R. Giegerich and J. Stoye, editors, GCB–2004, Proceedings of the German Conference on Bioinformatics, Lecture Notes in Informatics, pages 131–140, Bielefeld, Germany, October 2004. Springer-Verlag. [42% acceptance rate].

[33]E.H ullermeier.¨ Possibilistic instance-based learning. In Proceedings FGML–01, German Workshop on Machine Learning, pages 108–115, Dortmund, Germany, October 2001.

XVIII/XIX [34]E.H ullermeier.¨ Implication-based fuzzy association rules. In Proceedings FGML–01, German Workshop on Machine Learning, pages 93–99, Dortmund, Germany, October 2001.

[35]E.H ullermeier,¨ D. Dubois, and H. Prade. Instance-based prediction in the framework of possibility theory. In Proceedings of Workshop on Soft Computing in Case-Based Reasoning, Vanvouver, Canada, 2001.

[36] D. Dubois, E. Hullermeier,¨ and H. Prade. Flexible control of case-based prediction in the framework of possibility theory. In Proceedings RaPC–2000,` Raisonnement a` partir de Cas, pages 7–16, Toulouse, May 2000.

[37]E.H ullermeier,¨ D. Dubois, and H. Prade. Fuzzy rules in case-based reasoning. In Conferences´ AFIA–99, Proceedings RaPC–99,` Raisonnement a` partir de Cas, pages 45–54, Paris, Palaiseau, June 1999.

[38]E.H ullermeier.¨ Case-based probability and case-based decision making. In L. Gierl and M. Lenz, editors, Proceedings of the 6th German Workshop on Case-Based Reasoning, pages 13–22, Berlin, March 1998.

[39]E.H ullermeier.¨ Modellierung und Simulation unsicherer dynamischer Systeme und Anwendungen in der Diagnose. In D.P.F. Moller¨ and O. Richter, editors, ASIM Fachgruppentagung Soft Computing, April 1997.

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