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.
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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.
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Hardcover. Condizione: new. Hardcover. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780470229750
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