Computational Intelligence and Feature Selection (eBook)

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.