Computational Methods of Feature Selection

by Huan Liu (Editor), Hiroshi Motoda (Editor)

Overview

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the

Product Details

ISBN-13:	9781040211021
Publisher:	CRC Press
Publication date:	10/29/2007
Series:	Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Sold by:	Barnes & Noble
Format:	eBook
Pages:	440
File size:	10 MB

Table of Contents

Preface. Less Is More. Unsupervised Feature Selection. Randomized Feature Selection. Causal Feature Selection. Active Learning of Feature Relevance.A Study of Feature Extraction Techniques Based on Decision Border Estimate.Ensemble-Based Variable Selection Using Independent Probes.Efficient Incremental-Ranked Feature Selection in Massive Data.Non-Myopic Feature Quality Evaluation with (R)ReliefF.Weighting Method for Feature Selection in k-Means.Local Feature Selection for Classification.Feature Weighting through Local Learning.Feature Selection for Text Classification.A Bayesian Feature Selection Score Based on Naive Bayes Models.Pairwise Constraints-Guided Dimensionality Reduction.Aggressive Feature Selection by Feature Ranking.Feature Selection for Genomic Data Analysis.A Feature Generation Algorithm with Applications to Biological Sequence Classification.An Ensemble Method for Identifying Robust Features for Biomarker Discovery.Model Building and Feature Selection with Genomic Data. Index.

What People are Saying About This

From the Publisher

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

Editorial Reviews

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

From the Publisher