Feature selection for a nonlinear classifier

  • M. Sato
  • M. Kudo
  • J. Toyama
  • M. Shimbo
Feature Selection and Extraction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1451)

Abstract

The nonlinear classifier is effective for many practical problems. We have already proposed a method for constructing a nonlinear classifier using Legendre polynomials and have obtained good results on many actual data. In this approach, a set of original features is first extended to a large number of new features in a nonlinear fashion and then some substantial features are chosen for the nonlinear classifier. In this study, we have improved the selection process in the second stage by using some conventional feature selection algorithms. In addition, important features were selected from the original features in the preprocessing stage. The reduction in the number of the original features permits the nonlinear classifier to use a higher degree of polynomials.

Keywords

Feature Selection Training Sample Recognition Rate Legendre Polynomial Feature Selection Method 
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-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • M. Sato
    • 1
  • M. Kudo
    • 1
  • J. Toyama
    • 1
  • M. Shimbo
    • 2
  1. 1.C's lab, Ltd.SapporoJapan
  2. 2.Division of Systems and Information Engineering Graduate School of EngineeringHokkaido UniversitySapporoJapan

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