计算机科学与探索Issue(3)：345-351,7.DOI:10.3778/j.issn.1673-9418.1307019
决策粗糙集属性约简算法与属性核研究
Research on Attribute Reduction Algorithm and Attribute Core in Decision-Theoretic Rough Set
摘要
Abstract
Attribute reduction is one of the important research issues in rough set. In Pawlak rough set (PRS), the size of the positive region is monotonic with the increase of the attributes. However, the probabilistic positive region in decision-theoretic rough set (DTRS) may expand with the decrease of the attributes, which is essentially different from that of PRS model and leads to many different definitions of attribute reduction in DTRS model. To address this issue, this paper analyzes the problems of attribute reduction definition in DTRS, and illustrates the relation-ships among several attribute reductions. This paper also proves that a reduction which keeps the local maximal probabilistic positive regions has larger cost. Finally, this paper points out that a reduction which keeps the positive decision of all objects unchanged takes on the stability and exists the attribute core.关键词
决策粗糙集/属性约简/属性核Key words
decision-theoretic rough set/attribute reduction/attribute core分类
信息技术与安全科学引用本文复制引用
钱进,吕萍,岳晓冬..决策粗糙集属性约简算法与属性核研究[J].计算机科学与探索,2014,(3):345-351,7.基金项目
The National Natural Science Foundation of China under Grant Nos.61103067,61142007(国家自然科学基金) （国家自然科学基金）
the Foundation of Key Laboratory of Cloud Computing&Intelligent Information Processing of Changzhou under Grant No. CM20123004(常州市云计算与智能信息处理重点实验室项目) （常州市云计算与智能信息处理重点实验室项目）
the Natural Science Foundation of Jiangsu University of Technology under Grant No. kyy12018(江苏理工学院校基础自然科学资金项目) （江苏理工学院校基础自然科学资金项目）
the Doctoral Research Foundation of Jiangsu University of Technology under Grant No. kyy13003(江苏理工学院博士科研启动基金项目) （江苏理工学院博士科研启动基金项目）
