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

Authors:
Marek Kurzynski;Aleksander Rewak
Affiliations:
Chair of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland 50-370;Chair of Systems and Computer Networks, Wroclaw University of Technology, Wroclaw, Poland 50-370
Venue:
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 12
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Toward Bayes-Optimal Linear Dimension Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A Linear Feature Extraction for Multiclass Classification Problems Based on Class Mean and Covariance Discriminant Information

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
The bayes-optimal feature extraction procedure for pattern recognition using genetic algorithm

ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Gradual distributed real-coded genetic algorithms

IEEE Transactions on Evolutionary Computation
Dimensionality reduction using genetic algorithms

IEEE Transactions on Evolutionary Computation
A hybrid neural network/genetic algorithm approach to optimizing feature extraction for signal classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

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.