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Feature Selection by Combining Multiple Methods

  • Lior Rokach
  • Barak Chizi
  • Oded Maimon
Part of the Studies in Computational Intelligence book series (SCI, volume 23)

Summary

Feature selection is the process of identifying relevant features in the dataset and discarding everything else as irrelevant and redundant. Since feature selection reduces the dimensionality of the data, it enables the learning algorithms to operate more effectively and rapidly. In some cases, classification performance can be improved; in other instances, the obtained classifier is more compact and can be easily interpreted. There is much work done on feature selection methods for creating ensemble of classifiers. Thus, these works examine how feature selection can help ensemble of classifiers to gain diversity. This paper examines a different direction, i.e. whether ensemble methodology can be used for improving feature selection performance. In this paper we present a general framework for creating several feature subsets and then combine them into a single subset. Theoretical and empirical results presented in this paper validate the hypothesis that this approach can help finding a better feature subset.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lior Rokach
    • 1
  • Barak Chizi
    • 2
  • Oded Maimon
    • 2
  1. 1.Department of Information Systems EngineeringBen-Gurion University of the NegevIsrael
  2. 2.Department of Industrial EngineeringTel-Aviv UniversityIsrael

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