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Artificial Immune Systems for Information Filtering: Focusing on Profile Adaptation

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Artificial Immune Systems for Information Filtering: Focusing on Profile Adaptation ARTIFICIAL IMMUNE SYSTEMS FOR INFORMATION FILTERING: FOCUSING ON PROFILE ADAPTATION Nurulhuda Firdaus Mohd Azmi Doctor of Philosophy University of York Department of Computer Science January 2014 Abstract The human immune system has characteristics such as self-organisation, robust- ness and adaptivity that may be useful in the development of adaptive systems. One suitable application area for adaptive systems is Information Filtering (IF). Within the context of IF, learning and adapting user profiles is an important re- search area. In an individual profile, an IF system has to rely on the ability of the user profile to maintain a satisfactory level of filtering accuracy for as long as it is being used. This thesis explores a possible way to enable Artificial Immune Sys- tems (AIS) to filter information in the context of profile adaptation. Previous work has investigated this issue from the perspective of self-organisation based on Au- topoetic Theory. In contrast, this current work approaches the problem from the perspective of diversity inspired by the concept of dynamic clonal selection and gene library to maintain sufficient diversity. An immune-inspired IF for profile adaptation is proposed and developed. This algorithm is demonstrated to work in detecting relevant documents by using a single profile to recognize a user’s interests and to adapt to changes in them. We employed a virtual user tested on a web document corpus to test the profile on learning of an emerging new topic of interest and forgetting uninteresting topics. The results clearly indicate the pro- file’s ability to adapt to frequent variations and radical changes in user interest. This work has focused on textual information, but it may have the potential to be applied in other media such as audio and images in which adaptivity to dynamic environments is crucial. These are all interesting future directions in which this work might develop. i Contents Abstract i List of Tables vii List of Figures x List of Algorithms xiii Acknowledgment xiv Declaration xv 1 INTRODUCTION 1 1.1 Motivation and Background . 1 1.2 Challenges . 4 1.3 Research Questions . 5 1.4 Research Objectives . 5 1.5 Thesis Structure . 6 2 INFORMATION FILTERING 10 2.1 The general context of Information Filtering Systems . 10 2.1.1 Who initiates the Information Filtering Operation? . 12 2.1.2 Where Does Information Filtering Operate? . 12 2.1.3 What are the techniques for Information Filtering? . 13 2.1.4 How to Acquire Knowledge about a User? . 15 2.2 An Adaptive Information Filtering (AIF) . 16 2.3 User Profile . 19 2.3.1 The Challenges of Profile Adaptation . 19 2.3.2 Related Work on Adaptive User Profile . 20 ii CONTENTS 2.4 Algorithms for Learning Profile . 22 2.4.1 Profile Adaptation through Learning . 22 2.4.2 Profile Adaptation through Evolution . 23 2.4.3 Profile Adaptation through Connectionist Architecture . 25 2.5 Summary . 28 3 THE POTENTIAL OF ARTIFICIAL IMMUNE SYSTEMS (AIS) TO IN- FORMATION FILTERING (IF) 30 3.1 The Immune System in Context of the Biological Perspective . 31 3.1.1 Structure of the Immune System . 32 3.1.2 The Defence Layer . 32 3.1.3 Immune Cells . 34 3.1.4 The Immune Response . 35 3.2 AIS: Artificial Immune Systems . 37 3.2.1 Framework for Artificial Immune Systems . 38 3.3 The Potential of Artificial Immune Systems in Information Filter- ing: Application Review . 43 3.4 Principled Meta-Probes Applied to Adaptive Information Filtering (AIF) as a Source of Immune Inspiration . 45 3.4.1 ODISS Meta-Probes applied to Immune Systems . 45 3.4.2 ODISS Meta-Probes applied to Adaptive Information Fil- tering (AIF) . 48 3.4.3 ODISS Meta-Probes Mapped to Immune Inspired Adaptive Information Filtering . 51 3.5 The DCS: Dynamic Clonal Selection . 52 3.5.1 The Biological Inspiration of Clonal Selection . 53 3.6 Summary . 55 4 EXPERIMENTING WITH ARTIFICIAL IMMUNE SYSTEMS FOR IN- TEREST CLASSIFICATION 58 4.1 An Overview of the Artificial Immune Systems For Email Classifi- cation (AISEC) Algorithm . 59 4.1.1 Algorithms and Processes . 61 4.1.2 Parameters of the AISEC Algorithm . 63 4.2 The Extended Version of the AISEC Algorithm . 66 4.2.1 Extracting Semantic Concept From WordNet . 67 4.3 Comparing the classification performance of the AISEC versions for a Single Class of Email . 71 4.3.1 The Non-parametric Statistics . 73 iii CONTENTS 4.3.2 Matthew’s Correlation Coefficient (MCC) and Confusion Matrix . 75 4.3.3 Experimental Result . 77 4.4 Experimenting with the Extended AISEC on the Classification of Multiple Email Topics . 78 4.4.1 The Experimental Methodology . 80 4.4.2 The Baselines . 82 4.4.3 The Experiment Result and Analysis . 87 4.5 Summary . 93 5 EXPERIMENTING WITH THE EXTENDED AISEC’S PARAMETERS USING SENSITIVITY ANALYSIS 97 5.1 The Implementation of Sensitivity Analysis for Interest Classification 99 5.2 Analysis of Extended AISEC parameters in Single Topic Classifica- tion . 100 5.2.1 Experimental Result and Analysis . 103 5.2.2 Summary of the Experiment and the Assessment of the Op- timised Parameters . 110 5.3 Sensitivity Analysis on the Effect of Parameters in Multi Interest Classification . 114 5.3.1 Case 1: : Topics with Large Number of Emails with Topics with Low Number of Emails . 115 5.3.2 Assessment of the Optimised Parameter for the Case 1 Sce- nario . 119 5.3.3 Case 2: Both Topics have Low Numbers of emails . 121 5.3.4 Assessment of the Optimised Parameter for the Case 2 Sce- nario . 123 5.3.5 Case 3: Both Topics had High Numbers of emails . 125 5.3.6 Assessment of the Optimised Parameters for the Case 3 Sce- nario . 130 5.4 Summary . 131 6 EXPERIMENT ON PROFILE ADAPTATION THROUGH DYNAMIC CLONAL SELECTION 137 6.1 Dynamic Clonal Selection (DCS) . 139 6.1.1 DCS Potential for User Profile Adaptation . 140 6.2 An Overview of Profile Adaptation through Dynamic Clonal Se- lection (ProAdDCS) . 141 6.2.1 ProAdDCS and AISEC . 143 iv CONTENTS 6.2.2 The Flow Chart . 144 6.2.3 Representation . 144 6.2.4 The Affinity Function . 148 6.3 The process of ProAdDCS . 149 6.3.1 Initialisation . 149 6.3.2 Extracting Term from Initialisation Documents . 150 6.3.3 Running . 152 6.3.4 Establishing Population Dynamics . 156 6.3.5 Returning Result . 158 6.4 Algorithm Description for ProAdDCS . 158 6.5 Experimental Evaluation and Methodology . 161 6.5.1 The Comparative Approach . 164 6.6 Experimenting with ProAdDCS Profile in Adapting Learning and Forgetting Task. Case Study: TREC 2001 Filtering Track . 167 6.6.1 Task α: Parallel Interest of Two Topics . 169 6.6.2 Task β: New Topic of Interest Emerges . 173 6.6.3 Task γ: Forgetting Topic . 174 6.7 Experimenting with ProAdDCS Profile in Adapting Learning and Forgetting Task. Case Study: Reuters-21578 Document Collections 177 6.7.1 Experimental Methodology . 178 6.7.2 Experimental Results and Analysis . 180 6.8 Summary . 182 7 CONCLUSION 188 7.1 Thesis Contribution . 188 7.2 Limitations and Future Work . 192 7.3 Revisiting the Research Questions . 194 A Population-Based Immune-Inspired Information Filtering 199 B Network-Based Immune-Inspired Information Filtering 204 C Non-Parametric Statistical Analysis on Sensitivity Analysis for Single- Interest Classification 209 D Non-Parametric Statistical Analysis on Sensitivity Analysis for Multi- Interest Classification: Case 1 Scenario 211 E Non-Parametric Statistical Analysis on Sensitivity Analysis for Multi- Interest Classification: Case 2 Scenario 213 v CONTENTS F Non-Parametric Statistical Analysis on Sensitivity Analysis for Multi- Interest Classification: Case 3 Scenario 215 G The Java WordNet Library (JWNL) Interface 217 H List of Topic, Thematic Subject and Number of Document Description of the Topics for TREC-2001 Filtering Track 219 References 221 vi List of Tables 3.1 The differences between the Innate and Adaptive Immune Systems 34 3.2 Example of AIS Algorithms and their Corresponding Immune In- spiration . 38 3.3 ODISS characteristics of AIF as a source of immune inspiration [1] . 52 4.1 WordNet Relationship of Synset [2] . 68 4.2 Parameters Used for Testing AISEC After Parameter Optimisation . 73 4.3 The Vargha-Delaney A statistics Value and its Implication on Effect Size . 74 4.4 Statistical and Classification Performance Comparison of Original and Extended AISEC . 77 4.5 Example of semantic vectors representation for SRN. For the pur- pose of this illustration, not all the categories of an email topics are shown. 85 4.6 Results for single-topic experiments: Email Topic(first col.), MCC Score (two to fourth col.), differences in percentage between the ex- tended AISEC version with Naive Bayes (fifth col.), the SRN with Naive Bayes (sixth col.) and the extended AISEC version with SRN (seventh col.). 88 4.7 Results for two-topic experiments: Email Topic(first col.), MCC Score (two to fourth col.), differences in percentage between the ex- tended AISEC version with Naive Bayes (fifth col.), the SRN with Naive Bayes (sixth col.) and the extended AISEC version with SRN (seventh col.). 89 vii LIST OF TABLES 4.8 Results for three-topic experiments: Email Topic(first col.), MCC Score (two to fourth col.), differences in percentage between the ex- tended AISEC version with Naive Bayes (fifth col.), the SRN with Naive Bayes (sixth col.) and the extended AISEC version with SRN (seventh col.). 90 4.9 Results for four-topic experiments: Email Topic(first col.), MCC Score (two to fourth col.), differences in percentage between the ex- tended AISEC version with Naive Bayes (fifth col.), the SRN with Naive Bayes (sixth col.) and the extended AISEC version with SRN (seventh col.).
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