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Informationen zum Autor 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. Klappentext The rough and fuzzy set approaches presented here open up many new frontiers for continued research and developmentComputational 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 theoriesComputational 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. Zusammenfassung 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. Inhaltsverzeichnis PREFACE. 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 CLASSIFICAT...
