Description: Highlighting current research issues, this clear, concise, and coherent volume introduces the basic concepts and principles, state-of-the-art algorithms, and inventive applications of feature selection. With excellent surveys, practical guidance, and comprehensive tutorials from leading experts, it chronicles the novel developments of feature selection that have emerged in recent years, including causal feature selection and Relief. The book also presents the latest methodologies and algorithms, such as the Las Vegas, Monte Carlo, and Bayes risk-weighted vector quantization algorithms, and contains real-world case studies from a variety of areas, including text classification, web mining, and bioinformatics.
Review Quotes:
This book is a really comprehensive review of the modern techniques designed for feature selection in very large datasets. Dozens of algorithms and their comparisons in experiments with synthetic and real data are presented, which can be very helpful to researchers and students working with large data stores.
--Stan Lipovetsky, Technometrics, November 2010
Overall, we enjoyed reading this book. It presents state-of-the-art guidance and tutorials on methodologies and algorithms in computational methods in feature selection. Enhanced by the editors insights, and based on previous work by these leading experts in the field, the book forms another milestone of relevant research and development in feature selection.
--Longbing Cao and David Taniar, IEEE Intelligent Informatics Bulletin, 2008, Vol. 99, No. 99