Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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A Fuzzy-Rough Classification Method to Minimize the Coupling Problem of Rules

규칙의 커플링문제를 최소화하기 위한 퍼지-러프 분류방법

  • 손창식 (대구가톨릭대학교 컴퓨터정보통신공학부) ;
  • 정환묵 (대구가톨릭대학교 컴퓨터정보통신공학부) ;
  • 서석태 (영남대학교 전기공학과) ;
  • 권순학 (영남대학교 전기공학과)
  • Published : 2007.08.25
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Abstract

In this paper, we propose a novel pattern classification method based on statistical properties of the given data and fuzzy-rough set to minimize the coupling problem of the rules. In the proposed method, statistical properties is used by a selection criteria for deciding a partition number of antecedent fuzzy sets, and for minimizing an coupling problem of the generated rules. Moreover, rough set is used as a tool to remove unnecessary attributes between generated rules from the numerical data. In order to verify the validity of the proposed method, we compared the classification results (i.e, classification precision) of the proposed with the conventional pattern classification methods on the Fisher's IRIS data. From experiment results, we can conclude that the proposed method shows relatively better performance than those of the classification methods based on the conventional approaches.

본 논문에서는 규칙의 커플링 문제를 최소화하기 위해 주어진 데이터의 통계적 특성과 퍼지-러프집합을 기반으로 한 새로운 패턴분류 방법을 제안한다. 제안한 방법 하에서 주어진 데이터의 통계적 특성은 입력부 퍼지집합의 파티션 개수를 결정하고, 생성된 규칙의 커플링문제를 최소화하기 위한 선택기준으로 사용하였다. 또한 러프집합은 수치적인 데이터로부터 생성된 규칙들 간의 불필요한 속성들을 제거하기 위한 도구로서 이용하였다. 제안된 방법의 타당성을 검증하기 위하여 Fisher의 IRIS 데이터를 사용하여 기존의 패턴분류 방법과 분류 정확도를 비교하였다. 실험결과, 제안한 방법이 기존의 학습에 의한 방법들보다 비교적 좋은 성능을 가진다는 것을 알 수 있었다.

Keywords

References

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