Arizona State University, Tempe, AZ AFOSR/AOARD, Tokyo, Japan

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 2010Overall, 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

• 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 Naïve 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.

Arizona State University, Tempe, AZ AFOSR/AOARD, Tokyo, Japan

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 2010Overall, 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

• 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 Naïve 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.

Arizona State University, Tempe, AZ AFOSR/AOARD, Tokyo, Japan

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 2010Overall, 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

• 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 Naïve 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.