基于极大相容块的粗糙性度量及其属性约简  

Roughness measure and attribute reduction based on maximal consistent block

作　　者：江效尧[1] 程玉胜[2] 胡林生[1] 

机构地区：[1]南京审计学院信息科学学院,江苏南京210029 [2]安庆师范学院计算机与信息学院,安徽安庆246011

出　　处：《合肥工业大学学报（自然科学版）》2012年第4期476-480,共5页Journal of Hefei University of Technology：Natural Science

基　　金：安徽省自然科学基金资助项目(070412061);安徽省教育厅自然科学研究资助项目(2001kj161)

摘　　要：由于相似关系或相容关系不具有传递性或对称性,从而相容类或相似类之间存在误判,因此研究不完备信息系统中合适粒度下的粗糙性度量和属性约简算法很有必要。在不改变相关模型的基础上,文章通过极大相容块的思想,研究了非等价关系的基本知识粒度构造,进一步讨论了合适粒度下的粗糙性度量方法,提出了基于极大相容块的知识粗糙性更精确的定义和极大相容块的条件信息熵及其属性重要性定义,并证明了相关性质;给出了合适粒度下属性约简的启发式算法,结果表明,极大相容块的重要性度量避免了通常意义下粒度过粗问题,知识粗糙性更为准确。Due to the lack of transference and symmetry, there exists misjudgment in tolerance or simi- larity classes. Therefore, it is necessary to study roughness measure and attribute reduction algorithm with suitable granularity in incomplete information system. Without changing the relevant model, thispaper studies the basic knowledge granulation of non-equivalence relation such as the similarity or tol- erance classes, and the roughness measure method of knowledge with suitable granularity according to maximal consistent black（MCB）. Based on it, the more accurate definition about the roughness ofknowledge based on MCB, the conditional information entropy of MCB and its attribute significance are discussed. The relevant properties are proved and a heuristic algorithm of attribute reduction with suitable granularity is presented. The results show that the roughness measure based on MCB is more accurate, which can avoid the too rough granularity in the usual sense.

关 键 词：粗糙集理论 极大相容块 粗糙性度量 属性约简 条件信息熵 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]