Rough set theory is a relative new tool that deals with vagueness and uncertainty inherent in decision making. This paper introduce a new probabilistic approach for reducing dimensions and extracting rules of information systems using expert systems. The core of the approach is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules, Which is based on a combination of Generalized Distribution Table (GDT) and the Rough Set methodologies. The probabilistic properties of the Decision rules are discussed and the proposed probabilistic rough set approach was applied to discover grade rules of transformer evaluation when there is a missing failure symptom of transformer. The results show that the proposed approach represents explicitly the uncertainty of a rule, it can flexibly select biases for search control and it can effectively handle noisy and missing data.
목차
Abstract 1. Introduction 2. Rough Set and Missing Attribute Values 3. Generalized Distribution Table 4. Searching Algorithm for an Optimal Set of Rules 5. Conclusions References
보안공학연구지원센터(IJAST) [Science & Engineering Research Support Center, Republic of Korea(IJAST)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Advanced Science and Technology
간기
월간
pISSN
2005-4238
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Advanced Science and Technology vol.30