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Novel Methods for Feature Subset Selection with Respect to Problem Knowledge

  • Pavel Pudil
  • Jana Novovičová
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 453)

Abstract

Recent advances in the statistical methodology for selecting optimal subsets of features for data representation and classification are presented. This chapter attempts to provide a guideline of which approach to choose with respect to the extent of a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. One approach involves the use of the computationally effective Floating search methods. The alternative approach trades off the requirement for a priori information for the requirement of sufficient data to represent the distributions involved. Owing to its nature it is particularly suitable for cases when the underlying probability distributions are not unimodal. The approach attempts to achieve simultaneous feature selection and decision rule inference. According to the criterion adopted there are two variants allowing the selection of features either for optimal representation or discrimination.

Keywords

Feature Selection Feature Extraction Feature Subset Feature Selection Method Subset Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Pavel Pudil
    • 1
  • Jana Novovičová
    • 1
  1. 1.Institute of Information Theory and AutomationAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

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