Foundations of Soft Case-Based Reasoning (Hardcover)

Sankar K. Pal, Simon C. K. Shiu

  • 出版商: Wiley-Interscience
  • 出版日期: 2004-03-18
  • 售價: $980
  • 貴賓價: 9.5$931
  • 語言: 英文
  • 頁數: 274
  • 裝訂: Hardcover
  • ISBN: 0471086355
  • ISBN-13: 9780471086352

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A breakthrough on today’s fastest growing artificial intelligence technique

Many of today’s engineering and business computer applications require decisions to be made on the basis of uncertain or incomplete information, a phenomenon that has resulted in the development of case-based reasoning, a powerful computing technique by which a system’s problem-solving ability is enhanced by reference to its previously stored experiences.

Foundations of Soft Case-Based Reasoning is the first book of its kind to provide a unified framework for understanding how soft computing techniques can be used to build and maintain case-based reasoning (CBR) systems. Written by two internationally renowned experts, the book demonstrates the latest advances of machine learning and intelligence and presents CBR methodologies and algorithms designed to be useful for both students of artificial intelligence and practitioners in the field.

Structured according to the four major phases of the problem-solving process of a CBR system: representation and indexing of cases, case selection and retrieval, case adaptation, and case-base maintenance; the authors provide a solid foundation of the subject with a balanced mix of theory, algorithms, and application.

An important resource for students and practitioners alike, Foundations of Soft Case-Based Reasoning:

  • Illustrates decision-making problems in a wide range of engineering and business applications, such as data mining and pattern recognition
  • Features real-world examples of successful applications in such areas as medical diagnosis, weather prediction, law interpretation, and Web access path determination, to name a few
  • Describes in a unified way how the merits of various soft computing tools (e.g., fuzzy sets, neural networks, genetic algorithms, and rough sets) can give rise to more efficient CBR systems
  • Demonstrates the significance of granular computing and rough sets in CBR

Table of Contents:





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.



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.



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.



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.



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.



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.




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.



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.



C.1 Basic Principles.

C.2 Standard Genetic Algorithm.

C.3 Examples.

C.3.1 Function Maximization.

C.3.2 Traveling Salesman Problem.



D.1 Information Systems.

D.2 Indiscernibility Relation.

D.3 Set Approximations.

D.4 Rough Membership.

D.5 Dependency of Attributes.