摘要
现有的很多约简算法都是由构造决策表的区分矩阵出发,将矩阵中非空元素的合取范式转化为极小析取范式。但是,基于Skowron提出的区分矩阵约简算法对不相容决策表会产生错误的结果。为此,提出一种改进的区分矩阵的定义,以及基于此区分矩阵的属性约简算法,该算法对相容或不相容决策表都是适用的,特别对不相容决策表会得到更加稀疏的区分矩阵,可大大节省计算时间和存储空间,该算法是一种简单、有效、普遍适用的求解属性约简方法。
Many existing algorithms of attribute reduction begin at constructing decision table＇s discernibility matrix,then convert non-empty objects＇ conjunctive normal form into minimal disjunctive normal form.In order to correct the error of discernibility function method for attribute reduction based on Skowron＇s discernibility matrix which turns out to be error for inconsistent decision table,this paper proposes an improved discernibility matrix and the computation method on it,which suits for consistent or inconsistent data and is with lower complexity,especial to inconsistent data,it will get sparser matrix,so it is a simple and efficient method for attribute reduction.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第32期83-85,共3页
Computer Engineering and Applications
基金
国家自然科学基金委员会与中国民用航空总局联合资助项目(No.60672178)
中国民航大学博士启动基金资助项目(No.05qd02s)。
关键词
粗糙集
决策表
区分矩阵
属性约简
核
rough set
decision table
discernibility matrix
attribution reduction
core
