The feature extraction procedure for pattern recognition with learning using genetic algorithm

Authors:
Edward Puchala;Aleksander Rewak
Affiliations:
Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland;Wroclaw University of Technology, Faculty of Electronics, Wroclaw, Poland
Venue:
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Year:
2007

Citing 6
Cited 0

Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria

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)
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. In particular, the case of recognition with learning is considered. Bayes probability of correct classification is adopted as the extraction criterion. The problem with incomplete probabilistic information is discussed and Bayes-optimal feature extraction procedure is presented in detail. As method of solution of optimal feature extraction a genetic algorithm is proposed. A numerical example demonstrating quality of proposed algorithm to solve feature extraction problem is presented.