Computational Methods of Feature Selection
Published on 29. October 2007
440 pages
978-1-58488-879-6 (ISBN)
Description
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
Reviews / Votes
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
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Computer scientists and researchers in mathematics, statistics, engineering, and biomedical science.
Illustrations
91 s/w Abbildungen, 43 s/w Tabellen
91 b/w images, 43 tables and 300 equations
File size
14,85 MB
ISBN-13
978-1-58488-879-6 (9781584888796)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Huan Liu | Hiroshi Motoda
Computational Methods of Feature Selection
Book
10/2007
1st Edition
Chapman & Hall/CRC
€185.70
Shipment within 15-20 days
Content
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.
