Computational Intelligence and Feature Selection
Rough and Fuzzy Approaches
Will be published approx. on 17. October 2008
Book
Hardback
340 pages
978-0-470-22975-0 (ISBN)
Description
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.
More details
Product info
gebunden
Series
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Illustrations
Charts: 30 B&W, 0 Color; Photos: 2 B&W, 0 Color; Tables: 95 B&W, 0 Color; Graphs: 42 B&W, 0 Color
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 24 mm
Weight
701 gr
ISBN-13
978-0-470-22975-0 (9780470229750)
Schweitzer Classification
Other editions
Additional editions
Richard Jensen | Qiang Shen
Computational Intelligence and Feature Selection
Rough and Fuzzy Approaches
E-Book
10/2008
Wiley-IEEE Press
€138.99
Available for download
Persons
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.
Content
PREFACE.
1 THE IMPORTANCE OF FEATURE SELECTION.
1.1. Knowledge Discovery.
1.2. Feature Selection.
1.3. Rough Sets.
1.4. Applications.
1.5. Structure.
2 SET THEORY.
2.1. Classical Set Theory.
2.2. Fuzzy Set Theory.
2.3. Rough Set Theory.
2.4. Fuzzy-Rough Set Theory.
2.5. Summary.
3 CLASSIFICATION METHODS.
3.1. Crisp Approaches.
3.2. Fuzzy Approaches.
3.3. Rulebase Optimization.
3.4. Summary.
4 DIMENSIONALITY REDUCTION.
4.1. Transformation-Based Reduction.
4.2. Selection-Based Reduction.
4.3. Summary.
5 ROUGH SET BASED APPROACHES TO FEATURE SELECTION.
5.1. Rough Set Attribute Reduction.
5.2. RSAR Optimizations.
5.3. Discernibility Matrix Based Approaches.
5.4. Reduction with Variable Precision Rough Sets.
5.5. Dynamic Reducts.
5.6. Relative Dependency Method.
5.7. Tolerance-Based Method.
5.8. Combined Heuristic Method.
5.9. Alternative Approaches.
5.10. Comparison of Crisp Approaches.
5.11. Summary.
6 APPLICATIONS I: USE OF RSAR.
6.1. Medical Image Classification.
6.2. Text Categorization.
6.3. Algae Estimation.
6.4. Other Applications.
6.5. Summary.
7 ROUGH AND FUZZY HYBRIDIZATION.
7.1. Introduction.
7.2. Theoretical Hybridization.
7.3. Supervised Learning and Information Retrieval.
7.4. Feature Selection.
7.5. Unsupervised Learning and Clustering.
7.6. Neurocomputing.
7.7. Evolutionary and Genetic Algorithms.
7.8. Summary.
8 FUZZY-ROUGH FEATURE SELECTION.
8.1. Feature Selection with Fuzzy-Rough Sets.
8.2. Fuzzy-Rough Reduction Process.
8.3. Fuzzy-Rough QuickReduct.
8.4. Complexity Analysis.
8.5. Worked Examples.
8.6. Optimizations.
8.7. Evaluating the Fuzzy-Rough Metric.
8.8. Summary.
9 NEW DEVELOPMENTS OF FRFS.
9.1. Introduction.
9.2. New Fuzzy-Rough Feature Selection.
9.3. Experimentation.
9.4. Proofs.
9.5. Summary.
10 FURTHER ADVANCED FS METHODS.
10.1. Feature Grouping.
10.2. Ant Colony Optimization-Based Selection.
10.3. Summary.
11 APPLICATIONS II: WEB CONTENT CATEGORIZATION.
11.1. Text Categorization.
11.2. System Overview.
11.3. Bookmark Classification.
11.4. Web Site Classification.
11.5. Summary.
12 APPLICATIONS III: COMPLEX SYSTEMS MONITORING.
12.1. The Application.
12.2. Experimental Results.
12.3. Summary.
13 APPLICATIONS IV: ALGAE POPULATION ESTIMATION.
13.1. Application Domain.
13.2. Experimentation.
13.3. Summary.
14 APPLICATIONS V: FORENSIC GLASS ANALYSIS.
14.1. Background.
14.2. Estimation of Likelihood Ratio.
14.3. Application.
14.4. Experimentation.
14.5. Glass Classification.
14.6. Summary.
15 SUPPLEMENTARY DEVELOPMENTS AND INVESTIGATIONS.
15.1. RSAR-SAT.
15.2. Fuzzy-Rough Decision Trees.
15.3. Fuzzy-Rough Rule Induction.
15.4. Hybrid Rule Induction.
15.5. Fuzzy Universal Reducts.
15.6. Fuzzy-Rough Clustering.
15.7. Fuzzification Optimization.
15.8. Summary.
APPENDIX A: METRIC COMPARISON RESULTS: CLASSIFICATION DATASETS.
APPENDIX B: METRIC COMPARISON RESULTS: REGRESSION DATASETS.
REFERENCES.
INDEX.