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Zusatztext 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 Informationen zum Autor Huan Liu, Hiroshi Motoda Klappentext Feature selection is an essential step for successful data mining applications and has practical significance in many areas! such as statistics! pattern recognition! machine learning! and knowledge discovery. Through a clear! concise! and coherent presentation of topics! Computational Methods of Feature Selection systematically covers the key concepts! representative approaches! and inventive applications of various aspects of feature selection. The book bridges the widening gap between existing texts and rapid developments in the field by presenting recent research works from various disciplines. It features contributions from leading experts! along with real-world case studies. Zusammenfassung Feature selection is an essential step for successful data mining applications and has practical significance in many areas, such as statistics, pattern recognition, machine learning, and knowledge discovery. This book covers the key concepts, representative approaches, and inventive applications of various aspects of feature selection. Inhaltsverzeichnis 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. ...
