Fuzzy Transfer Learning

Fuzzy Transfer Learning Jethro Shell, B.A.(Hons.), M.Sc. Submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy. Faculty of Technology De Montfort University June 2013 Acknowledgements I would firstly like to thank Dr. Simon Coupland for his unwavering support through out this process alongside the rest of my supervisory team, Dr. Chi-Biu Wong and Dr. Eric Goodyer who assisted in supporting my endeavours. I would also like to thank those members, past and present, within the Centre for Compu- tational Intelligence at De Montfort University who offered assistance with this project, both technical and inspirational. A special mention goes to Prof. Bob John for financial and academic assistance, and Dr. Stephen Matthews, Dr. Hussam Hamrawi, Dr. Benjamin Passow and Dr. Stephen Vickers for their overall support. Special thanks goes to my parents, Gayle and Michael Shell alongside my brother Jacob and his family. All have have been life long inspirations and have offered unbounded support. The greatest thanks, however, goes to my wife Claire, who has been a rock of support through out from the onset. Without her unshakeable strength, love, and endless encouragement I would not have been able to complete this research. To her, I will be eternally grateful. i Abstract The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs). The sparsity of the data can be a result of the physical nature of the implementation, such as sensors placed into disaster recovery scenarios, or where the focus of the data acquisition is on very defined user groups, in the case of disabled individuals. Standard machine learning approaches focus on a need for training data to come from the same domain. The restrictions of the physical nature of these environments can severely reduce data acquisition making it extremely costly, or in certain situations, impossible. This impedes the ability of these approaches to model the environments. It is this problem, in the area of IEs, that this thesis is focussed. To address complex and uncertain environments, humans have learnt to use previously acquired information to reason and understand their surroundings. Knowledge from different but related domains can be used to aid the ability to learn. For example, the ability to ride a road bicycle can help when acquiring the more sophisticated skills of mountain biking. This humanistic approach to learning can be used to tackle real-world problems where a-priori labelled training data is either difficult or not possible to gain. The transferral of knowledge from a related, but differing context can allow for the reuse and repurpose of known information. In this thesis, a novel composition of methods are brought together that are broadly based on a humanist approach to learning. Two concepts, Transfer Learning (TL) and Fuzzy Logic (FL) are combined in a framework, Fuzzy Transfer Learning (FuzzyTL), to address the problem of learning tasks that have no prior direct contextual knowledge. Through the use of a FL based learning method, uncertainty that is evident in dynamic environments is represented. By combining labelled data from a contextually related source task, and little or no unlabelled data from a target task, the framework is shown to be able to accomplish predictive tasks using models learned from contextually different data. The framework incorporates an additional novel five stage online adaptation process. By adapting the underlying fuzzy structure through the use of previous labelled knowledge and new unlabelled information, an increase in predictive performance is shown. The framework outlined is applied to two differing real-world IEs to demonstrate its ability to predict in uncertain and dynamic environments. Through a series of experiments, it is shown that the framework is capable of predicting output using differing contextual data. ii Contents Acknowledgements ................................... i Abstract.......................................... ii 1 Introduction 1 1.1 ThesisSummary .................................. 3 1.2 Motivation...................................... 3 1.3 Hypotheses ..................................... 5 1.4 Major Contributions of the Thesis . ...... 6 1.5 StructureoftheThesis .... ...... ..... ...... ..... .. ... 6 2 Literature Review 8 2.1 Introduction.................................... 8 2.2 Context ....................................... 8 2.2.1 Applications and Implementations of Context . ........ 9 2.2.2 DefiningContext .............................. 10 2.2.3 Uncertainty in Context . 11 2.2.4 Discussion ................................. 11 2.3 FuzzyLogic..................................... 11 2.3.1 Uncertainty................................. 12 2.3.2 FuzzyLogicSets .............................. 13 2.3.3 FuzzyRules................................. 16 2.3.4 Fuzzy Inference System . 17 2.3.5 Rule Induction Methods . 18 2.3.5.1 Wang-Mendel Methodology . 19 2.3.5.2 Other Rule Induction Methods . 21 2.3.6 FuzzyClustering .............................. 22 2.3.7 NeuralNetworks .............................. 24 2.3.8 Evolutionary Computation . 25 2.3.9 Online and Evolving Fuzzy Systems . 26 iii 2.3.10 Discussion ................................. 27 2.4 TransferLearning................................ 28 2.4.1 Measures, Definition and Foundations . ..... 29 2.4.2 Background................................. 30 2.4.3 Transfer Learning Types and Variations . ...... 31 2.4.3.1 Unsupervised Transfer Learning . 31 2.4.3.2 Inductive Transfer Learning . 32 2.4.3.3 Transductive Transfer Learning . 32 2.4.3.4 Negative Learning . 32 2.4.3.5 Limited-Data Transfer Learning Methods . 33 2.4.3.6 Transfer Learning With Computational Intelligence ...... 34 2.4.4 OtherLearningMethods . 36 2.4.4.1 Semi-Supervised Learning . 36 2.4.4.2 Domain Adaptation . 37 2.4.5 Discussion ................................. 37 2.5 Intelligent Environments . ..... 38 2.5.1 Definition.................................. 38 2.5.2 Computational Intelligence in Intelligent Environments.......... 40 2.5.2.1 Multi-Agent Adaptive Fuzzy Systems . 40 2.5.2.2 Fuzzy Logic Systems for Prediction . 43 2.5.2.3 Interval and General Type-2 Fuzzy Logic Implementations . 43 2.5.3 Discussion ................................. 45 3 Fuzzy Transfer Learning 46 3.1 Introduction.................................... 46 3.2 Overview ...................................... 46 3.3 Definitions...................................... 48 3.4 Transferring Fuzzy Concepts . ..... 49 3.4.1 Transferring Knowledge Through a Fuzzy Logic System . ........ 49 3.4.2 Extending the Wang-Mendel Method: Fuzzy Frequency Rule Pruning . 50 3.5 Adaptation Through Learning . 55 3.5.1 External Input Domain Adjustment: Stage One . ...... 56 3.5.2 Internal Input Domain Adjustment: Stage Two . ...... 59 3.5.3 Output Domain Adaptation Through Gradient Control : Stage Three . 62 3.5.4 Rule Base Modification Via Source Rule Comparison : Stage Four . 66 3.5.5 Rule Adaptation Using Euclidean Distance Measure : Stage Five . 68 3.6 Summary ...................................... 70 iv 3.6.1 Summary of Transferring Fuzzy Concepts . ..... 70 3.6.2 Summary of Adaptation Through Learning . 71 4 Fuzzy Transfer Learning in Intelligent Environments 72 4.1 Introduction.................................... 72 4.2 ExperimentalDesign .............................. 72 4.2.1 Intel Berkeley Research Laboratory Dataset . ........ 74 4.2.2 De Montfort University Robotics Laboratory Dataset . .......... 75 4.2.3 ExperimentStructure. 79 4.3 Performance..................................... 82 4.3.1 Intel Laboratory Data Comparison to Observed Values . ......... 83 4.3.2 Robotics Laboratory Data Comparison to Observed Values........ 90 4.3.3 SummaryofResults ............................ 94 4.4 ContextImpact ................................... 95 4.4.1 Inter Contextual Experiments . 95 4.4.2 Intra Contextual Experiments . 100 4.4.3 SummaryofResults ............................ 103 4.5 Adaptation...................................... 104 4.5.1 Comparison of Non-Adaptive FuzzyTL to Full FuzzyTL Framework: Intel LaboratoryData .............................. 104 4.5.2 Comparison of Non-Adaptive FuzzyTL to Full FuzzyTL Framework: Robotics Laboratory Data . 109 4.5.3 SummaryofResults ............................ 112 4.6 Summary of the Application of Fuzzy Transfer Learning in Intelligent Environments113 4.6.1 Summary of Performance Experimental Process . 113 4.6.2 Summary of Context Impact Experimental Process . ....... 114 4.6.3 Summary of Adaptation Experimental Process . 115 5 Conclusion and Future Work 116 5.1 Conclusion ..................................... 116 5.1.1 Contextually Differing Environments Can Act as Source Information . 117 5.1.2 Contextual Distance Has Little Effect on Error . ........ 118 5.1.3 Online Adaptation Decreases the Error of the FuzzyTL Output . 119 5.1.4 MajorContributions . 120 5.2 Recommendations and Future Work . 122 Bibliography 125 v List of Figures 1.1 FloorPlanofHomeAandHomeB . 4 2.1 Example of a Function as Depicted as a Classical Set. ........... 14 2.2 Example of a Triangular Membership Function. ......... 15 2.3 Height of an Individual Expressed as Fuzzy Sets. .......... 15 2.4 The Structure of a Fuzzy Inference System Adapted From

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