The rough and fuzzy set approaches presented here open up many
new frontiers for continued research and development

Computational Intelligence and Feature Selection provides
readers with the background and fundamental ideas behind Feature
Selection (FS), with an emphasis on techniques based on rough and
fuzzy sets. For readers who are less familiar with the subject, the
book begins with an introduction to fuzzy set theory and
fuzzy-rough set theory. Building on this foundation, the book
provides:

* A critical review of FS methods, with particular emphasis on
their current limitations

* Program files implementing major algorithms, together with the
necessary instructions and datasets, available on a related Web
site

* Coverage of the background and fundamental ideas behind FS

* A systematic presentation of the leading methods reviewed in a
consistent algorithmic framework

* Real-world applications with worked examples that illustrate the
power and efficacy of the FS approaches covered

* An investigation of the associated areas of FS, including rule
induction and clustering methods using hybridizations of fuzzy and
rough set theories

Computational Intelligence and Feature Selection is an
ideal resource for advanced undergraduates, postgraduates,
researchers, and professional engineers. However, its
straightforward presentation of the underlying concepts makes the
book meaningful to specialists and nonspecialists alike.

Richard Jensen, PhD, is a Lecturer with the Department of
Computer Science at Aberystwyth University, United Kingdom. Dr.
Jensen has published extensively in the subject area of Feature
Selection. Additionally, he has taught master's courses in
engineering knowledge-based systems and served as supervisor for
many student projects on Feature Selection, fuzzy-rough systems
modeling, and swarm intelligence at both the University of
Edinburgh, Scotland, and the University of Wales.

Qiang Shen, PhD, is Professor and Director of Research
with the Department of Computer Science at Aberystwyth University,
and an Honorary Fellow at the University of Edinburgh. Dr. Shen's
research interests include artificial and computational
intelligence. He is an associate editor and editorial board member
of several world-leading journals and has been a chair or cochair
of many national and international conferences in his research
area.

Computational Intelligence and Feature Selection provides readers with the background and fundamental ideas behind Feature Selection (FS), with an emphasis on techniques based on rough and fuzzy sets. For readers who are less familiar with the subject, the book begins with an introduction to fuzzy set theory and fuzzy-rough set theory. Building on this foundation, the book provides:

• A critical review of FS methods, with particular emphasis on their current limitations

• Program files implementing major algorithms, together with the necessary instructions and datasets, available on a related Web site

• Coverage of the background and fundamental ideas behind FS

• A systematic presentation of the leading methods reviewed in a consistent algorithmic framework

• Real-world applications with worked examples that illustrate the power and efficacy of the FS approaches covered

• An investigation of the associated areas of FS, including rule induction and clustering methods using hybridizations of fuzzy and rough set theories

Computational Intelligence and Feature Selection is an ideal resource for advanced undergraduates, postgraduates, researchers, and professional engineers. However, its straightforward presentation of the underlying concepts makes the book meaningful to specialists and nonspecialists alike.

1 THE IMPORTANCE OF FEATURE SELECTION.

1.1. Knowledge Discovery.

1.2. Feature Selection.

1.2.1. The Task.

1.2.2. The Benefits.

1.3. Rough Sets.

1.4. Applications.

1.5. Structure.

2 SET THEORY.

2.1. Classical Set Theory.

2.1.1. Definition.

2.1.2. Subsets.

2.1.3. Operators.

2.2. Fuzzy Set Theory.

2.2.1. Definition.

2.2.2. Operators.

2.2.3. Simple Example.

2.2.4. Fuzzy Relations and Composition.

2.2.5. Approximate Reasoning.

2.2.6. Linguistic Hedges.

2.2.7. Fuzzy Sets and Probability.

2.3. Rough Set Theory.

2.3.1. Information and Decision Systems.

2.3.2. Indiscernibility.

2.3.3. Lower and Upper Approximations.

2.3.4. Positive, Negative, and Boundary Regions.

2.3.5. Feature Dependency and Significance.

2.3.6. Reducts.

2.3.7. Discernibility Matrix.

2.4. Fuzzy-Rough Set Theory.

2.4.1. Fuzzy Equivalence Classes.

2.4.2. Fuzzy-Rough Sets.

2.4.3. Rough-Fuzzy Sets.

2.4.4. Fuzzy-Rough Hybrids.

2.5. Summary.

3 CLASSIFICATION METHODS.

3.1. Crisp Approaches.

3.1.1. Rule Inducers.

3.1.2. Decision Trees.

3.1.3. Clustering.

3.1.4. Naive Bayes.

3.1.5. Inductive Logic Programming.

3.2. Fuzzy Approaches.

3.2.1. Lozowski's Method.

3.2.2. Subsethood-Based Methods.

3.2.3. Fuzzy Decision Trees.

3.2.4. Evolutionary Approaches.

3.3. Rulebase Optimization.

3.3.1. Fuzzy Interpolation.

3.3.2. Fuzzy Rule Optimization.

3.4. Summary.

4 DIMENSIONALITY REDUCTION.

4.1. Transformation-Based Reduction.

4.1.1. Linear Methods.

4.1.2. Nonlinear Methods.

4.2. Selection-Based Reduction.

4.2.1. Filter Methods.

4.2.2. Wrapper Methods.

4.2.3. Genetic Approaches.

4.2.4. Simulated Annealing Based Feature Selection.

4.3. Summary.

5 ROUGH SET BASED APPROACHES TO FEATURE SELECTION.

5.1. Rough Set Attribute Reduction.

5.1.1. Additional Search Strategies.

5.1.2. Proof of QUICKREDUCT Monotonicity.

5.2. RSAR Optimizations.

5.2.1. Implementation Goals.

5.2.2. Implementational Optimizations.

5.3. Discernibility Matrix Based Approaches.

5.3.1. Johnson Reducer.

5.3.2. Compressibility Algorithm.

5.4. Reduction with Variable Precision Rough Sets.

5.5. Dynamic Reducts.

5.6. Relative Dependency Method.

5.7. Tolerance-Based Method.

5.7.1. Similarity Measures.

5.7.2. Approximations and Dependency.

5.8. Combined Heuristic Method.

5.9. Alternative Approaches.

5.10. Comparison of Crisp Approaches.

5.10.1. Dependency Degree Based Approaches.

5.10.2. Discernibility Matrix Based Approaches.

5.11. Summary.

6 APPLICATIONS I: USE OF RSAR.

6.1. Medical Image Classification.

6.1.1. Problem Case.

6.1.2. Neural Network Modeling.

6.1.3. Results.

6.2. Text Categorization.

6.2.1. Problem Case.

6.2.2. Metrics.

6.2.3. Datasets Used.

6.2.4. Dimensionality Reduction.

6.2.5. Information Content of Rough Set Reducts.

6.2.6. Comparative Study of TC Methodologies.

6.2.7. Efficiency Considerations of RSAR.

6.2.8. Generalization.

6.3. Algae Estimation.

6.3.1. Problem Case.

6.3.2. Results.

6.4. Other Applications.

6.4.1. Prediction of Business Failure.

6.4.2. Fina...