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

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.

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