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http://dx.doi.org/10.5391/JKIIS.2008.18.1.094

Feature Selection by Genetic Algorithm and Information Theory  

Cho, Jae-Hoon (충북대학교 전기전자컴퓨터공학부)
Lee, Dae-Jong (충북대학교 BK2l 충북정보기술사업단)
Song, Chang-Kyu (충북대학교 BK2l 충북정보기술사업단)
Kim, Yong-Sam (충북대학교 전기전자컴퓨터공학부)
Chun, Myung-Geun (충북대학교 전기전자컴퓨터공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.1, 2008 , pp. 94-99 More about this Journal
Abstract
In the pattern classification problem, feature selection is an important technique to improve performance of the classifiers. Particularly, in the case of classifying with a large number of features or variables, the accuracy of the classifier can be improved by using the relevant feature subset to remove the irrelevant, redundant, or noisy data. In this paper we propose a feature selection method using genetic algorithm and information theory. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.
Keywords
Feature selection; Pattern classification; Genetic algorithm; Mutual information;
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