Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches: 8 (IEEE Press Series on Computational Intelligence) - Tapa dura

Acerca del 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.

De la contraportada

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.