Foundations of Soft Case-Based Reasoning.

FOUNDATIONS OF SOFT CASE-BASED REASONING -- CONTENTS -- FOREWORD -- PREFACE -- ABOUT THE AUTHORS -- 1 INTRODUCTION -- 1.1 Background -- 1.2 Components and Features of Case-Based Reasoning -- 1.2.1 CBR System versus Rule-Based System -- 1.2.2 CBR versus Human Reasoning -- 1.2.3 CBR Life Cycle -- 1.3 Guidelines for the Use of Case-Based Reasoning -- 1.4 Advantages of Using Case-Based Reasoning -- 1.5 Case Representation and Indexing -- 1.5.1 Case Representation -- 1.5.2 Case Indexing -- 1.6 Case Retrieval -- 1.7 Case Adaptation -- 1.8 Case Learning and Case-Base Maintenance -- 1.8.1 Learning in CBR Systems -- 1.8.2 Case-Base Maintenance -- 1.9 Example of Building a Case-Based Reasoning System -- 1.9.1 Case Representation -- 1.9.2 Case Indexing -- 1.9.3 Case Retrieval -- 1.9.4 Case Adaptation -- 1.9.5 Case-Base Maintenance -- 1.10 Case-Based Reasoning: Methodology or Technology? -- 1.11 Soft Case-Based Reasoning -- 1.11.1 Fuzzy Logic -- 1.11.2 Neural Networks -- 1.11.3 Genetic Algorithms -- 1.11.4 Some CBR Tasks for Soft Computing Applications -- 1.12 Summary -- References -- 2 CASE REPRESENTATION AND INDEXING -- 2.1 Introduction -- 2.2 Traditional Methods of Case Representation -- 2.2.1 Relational Representation -- 2.2.2 Object-Oriented Representation -- 2.2.3 Predicate Representation -- 2.2.4 Comparison of Case Representations -- 2.3 Soft Computing Techniques for Case Representation -- 2.3.1 Case Knowledge Representation Based on Fuzzy Sets -- 2.3.2 Rough Sets and Determining Reducts -- 2.3.3 Prototypical Case Generation Using Reducts with Fuzzy Representation -- 2.4 Case Indexing -- 2.4.1 Traditional Indexing Method -- 2.4.2 Case Indexing Using a Bayesian Model -- 2.4.3 Case Indexing Using a Prototype-Based Neural Network -- 2.4.4 Case Indexing Using a Three-Layered Back Propagation Neural Network -- 2.5 Summary -- References.

3 CASE SELECTION AND RETRIEVAL -- 3.1 Introduction -- 3.2 Similarity Concept -- 3.2.1 Weighted Euclidean Distance -- 3.2.2 Hamming and Levenshtein Distances -- 3.2.3 Cosine Coefficient for Text-Based Cases -- 3.2.4 Other Similarity Measures -- 3.2.5 k-Nearest Neighbor Principle -- 3.3 Concept of Fuzzy Sets in Measuring Similarity -- 3.3.1 Relevance of Fuzzy Similarity in Case Matching -- 3.3.2 Computing Fuzzy Similarity Between Cases -- 3.4 Fuzzy Classification and Clustering of Cases -- 3.4.1 Weighted Intracluster and Intercluster Similarity -- 3.4.2 Fuzzy ID3 Algorithm for Classification -- 3.4.3 Fuzzy c-Means Algorithm for Clustering -- 3.5 Case Feature Weighting -- 3.5.1 Using Gradient-Descent Technique and Neural Networks -- 3.5.2 Using Genetic Algorithms -- 3.6 Case Selection and Retrieval Using Neural Networks -- 3.6.1 Methodology -- 3.6.2 Glass Identification -- 3.7 Case Selection Using a Neuro-Fuzzy Model -- 3.7.1 Selection of Cases and Class Representation -- 3.7.2 Formulation of the Network -- 3.8 Case Selection Using Rough-Self Organizing Map -- 3.8.1 Pattern Indiscernibility and Fuzzy Discretization of Feature Space -- 3.8.2 Methodology for Generation of Reducts -- 3.8.3 Rough SOM -- 3.8.4 Experimental Results -- 3.9 Summary -- References -- 4 CASE ADAPTATION -- 4.1 Introduction -- 4.2 Traditional Case Adaptation Strategies -- 4.2.1 Reinstantiation -- 4.2.2 Substitution -- 4.2.3 Transformation -- 4.2.4 Example of Adaptation Knowledge in Pseudocode -- 4.3 Some Case Adaptation Methods -- 4.3.1 Learning Adaptation Cases -- 4.3.2 Integrating Rule- and Case-Based Adaptation Approaches -- 4.3.3 Using an Adaptation Matrix -- 4.3.4 Using Configuration Techniques -- 4.4 Case Adaptation Through Machine Learning -- 4.4.1 Fuzzy Decision Tree -- 4.4.2 Back-Propagation Neural Network -- 4.4.3 Bayesian Model -- 4.4.4 Support Vector Machine.

4.4.5 Genetic Algorithms -- 4.5 Summary -- References -- 5 CASE-BASE MAINTENANCE -- 5.1 Introduction -- 5.2 Background -- 5.3 Types of Case-Base Maintenance -- 5.3.1 Qualitative Maintenance -- 5.3.2 Quantitative Maintenance -- 5.4 Case-Base Maintenance Using a Rough-Fuzzy Approach -- 5.4.1 Maintaining the Client Case Base -- 5.4.2 Experimental Results -- 5.4.3 Complexity Issues -- 5.5 Case-Base Maintenance Using a Fuzzy Integral Approach -- 5.5.1 Fuzzy Measures and Fuzzy Integrals -- 5.5.2 Case-Base Competence -- 5.5.3 Fuzzy Integral-Based Competence Model -- 5.5.4 Experiment Results -- 5.6 Summary -- References -- 6 APPLICATIONS -- 6.1 Introduction -- 6.2 Web Mining -- 6.2.1 Case Representation Using Fuzzy Sets -- 6.2.2 Mining Fuzzy Association Rules -- 6.3 Medical Diagnosis -- 6.3.1 System Architecture -- 6.3.2 Case Retrieval Using a Fuzzy Neural Network -- 6.3.3 Case Evaluation and Adaptation Using Induction -- 6.4 Weather Prediction -- 6.4.1 Structure of the Hybrid CBR System -- 6.4.2 Case Adaptation Using ANN -- 6.5 Legal Inference -- 6.5.1 Fuzzy Logic in Case Representation -- 6.5.2 Fuzzy Similarity in Case Retrieval and Inference -- 6.6 Property Valuation -- 6.6.1 PROFIT System -- 6.6.2 Fuzzy Preference in Case Retrieval -- 6.7 Corporate Bond Rating -- 6.7.1 Structure of a Hybrid CBR System Using GAs -- 6.7.2 GA in Case Indexing and Retrieval -- 6.8 Color Matching -- 6.8.1 Structure of the Color-Matching Process -- 6.8.2 Fuzzy Case Retrieval -- 6.9 Shoe Design -- 6.9.1 Feature Representation -- 6.9.2 Neural Networks in Retrieval -- 6.10 Other Applications -- 6.11 Summary -- References -- APPENDIXES -- A FUZZY LOGIC -- A.1 Fuzzy Subsets -- A.2 Membership Functions -- A.3 Operations on Fuzzy Subsets -- A.4 Measure of Fuzziness -- A.5 Fuzzy Rules -- A.5.1 Definition -- A.5.2 Fuzzy Rules for Classification -- References.

B ARTIFICIAL NEURAL NETWORKS -- B.1 Architecture of Artificial Neural Networks -- B.2 Training of Artificial Neural Networks -- B.3 ANN Models -- B.3.1 Single-Layered Perceptron -- B.3.2 Multilayered Perceptron Using a Back-Propagation Algorithm -- B.3.3 Radial Basis Function Network -- B.3.4 Kohonen Neural Network -- References -- C GENETIC ALGORITHMS -- C.1 Basic Principles -- C.2 Standard Genetic Algorithm -- C.3 Examples -- C.3.1 Function Maximization -- C.3.2 Traveling Salesman Problem -- References -- D ROUGH SETS -- D.1 Information Systems -- D.2 Indiscernibility Relation -- D.3 Set Approximations -- D.4 Rough Membership -- D.5 Dependency of Attributes -- References -- INDEX.