eBook - Computational Intelligence and Feature Selection : Rough and Fuzzy Approaches
Available Exclusively as an eBook | Part of the AI eBook Collection | Only for Institutional Purchase | Publication Year: 2008
Description
Computational Intelligence and Feature Selection explores feature selection techniques based on rough and fuzzy set theories. It reviews existing methods, presents algorithms, discusses real-world applications, and investigates related areas like rule induction and clustering. The book serves advanced students, researchers, and professionals, offering foundational knowledge and practical insights into fuzzy-rough set approaches for intelligent data analysis.
Table of Contents
1 THE IMPORTANCE OF FEATURE SELECTION.
2 SET THEORY.
3 CLASSIFICATION METHODS.
4 DIMENSIONALITY REDUCTION.
5 ROUGH SET BASED APPROACHES TO FEATURE SELECTION.
6 APPLICATIONS I: USE OF RSAR.
7 ROUGH AND FUZZY HYBRIDIZATION.
8 FUZZY-ROUGH FEATURE SELECTION.
9 NEW DEVELOPMENTS OF FRFS.
10 FURTHER ADVANCED FS METHODS.
11 APPLICATIONS II: WEB CONTENT CATEGORIZATION.
12 APPLICATIONS III: COMPLEX SYSTEMS MONITORING.
13 APPLICATIONS IV: ALGAE POPULATION ESTIMATION.
14 APPLICATIONS V: FORENSIC GLASS ANALYSIS.
15 SUPPLEMENTARY DEVELOPMENTS AND INVESTIGATIONS.
APPENDIX A: METRIC COMPARISON RESULTS: CLASSIFICATION DATASETS.
APPENDIX B: METRIC COMPARISON RESULTS: REGRESSION DATASETS.
REFERENCES.
INDEX