Computational Methods of Feature Selection

Huan Liu, Hiroshi Motoda

October 29, 2007 by Chapman and Hall/CRC
Reference - 440 Pages - 91 B/W Illustrations
ISBN 9781584888789 - CAT# C8784
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Features

  • Features excellent surveys, practical guidance, and comprehensive tutorials from leading experts
  • Chronicles the novel developments of feature selection that have emerged in recent years, including causal feature selection and Relief
  • Presents state-of-the-art methodologies and algorithms, such as the Las Vegas, Monte Carlo, and Bayes risk-weighted vector quantization algorithms
  • Contains real-world case studies from a variety of areas, including text classification, web mining, and bioinformatics
  • Summary

    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.