Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches

This book 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. Next it provides a critical review of FS methods with an emphasis on their current limitations, program files used to implement major algorithms, and associated areas of FS, including rule induction and clustering methods using hybridization of fuzzy and rough set theories.

Nyckelord: machine learning, pattern recognition, systems control, signal processing, set theory, fuzzy set theory, rough set theory, fuzzy rough set theory, computational intelligence, fuzzy rule induction, swarm optimization, feature selection algorithm, Computational Intelligence, neural networks, feature selection classification, artificial intelligence, network intelligence, AI, data intelligence