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The GA-Based Bayes-Optimal Feature Extraction Procedure Applied to the Supervised Pattern Recognition

  • Marek Kurzynski
  • Aleksander Rewak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5097)

Abstract

The paper deals with the extraction of features for statistical pattern recognition. Bayes probability of correct classification is adopted as the extraction criterion. The problem with complete probabilistic information is discussed and next the Bayes-optimal feature extraction procedure for the supervised classfication is presented in detail. As method of solution of optimal feature extraction a genetic algorithm is proposed. Several computer experiments for wide spectrum of cases were made and their results demonstrating capability of proposed approach to solve feature extraction problem are presented.

Keywords

Genetic Algorithm Feature Extraction Linear Discriminant Analysis Principal Component Analysis Method Statistical Pattern Recognition 
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 2008

Authors and Affiliations

  • Marek Kurzynski
    • 1
  • Aleksander Rewak
    • 1
  1. 1.Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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