베이지언 정보엔트로피에 의한 불완전 의사결정 시스템의 불확실성 향상

Abstract

Abstract Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system. Keywords : Rough Set, Indiscernibility Relation, Conditional Information Entropy, Uncertainty, Bayesian Theory * 종신회원, 청운대학교 컴퓨터학과 ** 정회원, 중부대학교 컴퓨터학과 (교신저자)접수일자: 2014년 11월 11일, 수정일자: 2014년 12월 9일게재확정일자: 2014년 12월 12일 Received: 11 November, 2014 / Revised: 9 December, 2014Accepted: 12 December, 2014