Neuware - 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.

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Bibliographic Details

Title: Computational Methods of Feature Selection
Publisher: Taylor & Francis Inc Okt 2007
Publication Date: 2007
Language: English
ISBN 10: 1584888784
ISBN 13: 9781584888789
Binding: Buch
Condition: Neu
Dimensions: 237 millimeters width by 163 millimeters height by 29 millimeters depth
Weight: 766 grams

About this title

Synopsis

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 basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool.

The book begins by exploring unsupervised, randomized, and causal feature selection. It then reports on some recent results of empowering feature selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection.

Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.

About the Author

Huan Liu, Hiroshi Motoda

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