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Feature Selection for Classification Using Genetic Algorithms with a Novel Encoding

  • Franz Pernkopf
  • Paul O’Leary
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2124)

Abstract

Genetic algorithms with a novel encoding scheme for feature selection are introduced. The proposed genetic algorithm is restricted to a particular predetermined feature subset size where the local optimal set of features is searched for. The encoding scheme limits the length of the individual to the specified subset size, whereby each gene has a value in the range from 1 to the total number of available features.

This article also gives a comparative study of suboptimal feature selection methods using real-world data. The validation of the optimized results shows that the true feature subset size is significantly smaller than the global optimum found by the optimization algorithms.

Keywords

pattern recognition feature selection genetic algorithm 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Franz Pernkopf
    • 1
  • Paul O’Leary
    • 1
  1. 1.University of LeobenLeobenAustria

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