Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Feature Selection by Genetic Algorithm and Information Theory

유전자 알고리즘과 정보이론을 이용한 속성선택

  • 조재훈 (충북대학교 전기전자컴퓨터공학부) ;
  • 이대종 (충북대학교 BK2l 충북정보기술사업단) ;
  • 송창규 (충북대학교 BK2l 충북정보기술사업단) ;
  • 김용삼 (충북대학교 전기전자컴퓨터공학부) ;
  • 전명근 (충북대학교 전기전자컴퓨터공학부)
  • Published : 2008.02.25
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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.

속성선택 (Feature Selection)은 패턴분류 문제에서 분류기들의 성능을 향상시킬 수 있는 중요한 기법이다. 특히, 많은 속성들을 가지는 데이터의 분류문제에서 관련이 적은 데이터, 중복되거나 또는 노이즈 있는 데이터를 제거한 주요 속성부분집합을 선택하여 이용함으로써 분류기의 정확도를 향상시킬 수 있다. 본 논문에서는 유전자 알고리즘과 정보이론의 상호정보량을 이용하여 속성선택을 하는 기법을 제안하였다. 실험을 통하여 제안된 알고리즘이 패턴인식문제에서 다른 방법들보다 성능이 우수함을 보였다.

Keywords

References

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