Abstract:
This paper expounds the basic conceptions of the rough set theory and information entropy. In order to find the effective approach of attribute reduction, an algorithm of attribute reduction based on rough set and information entropy is put forward. In decision table, a size of mutual information caused by an attribute reflects on the attribute significance, and gets the relative reduction. The studies show that the algorithm not only can get the optimal decision rules, but also can greatly decrease search space that the information system requires, and get more perfect attribute reduction effect.
Key words:
rough set theory,
information entropy,
attribute reduction,
information system
摘要: 阐述粗糙集理论和信息熵的基本概念,并为寻找属性约简的有效方法,提出一种基于粗糙集和信息熵的属性约简算法。在决策表中添加某个属性引起的互信息变化的大小,以反映该属性的重要性,并求相对约简。研究表明,该算法不仅能得到最优的决策规则,而且能够减少信息系统所需的搜索空间,得到更优的属性约简效果。
关键词:
粗糙集理论,
信息熵,
属性约简,
信息系统
CLC Number:
TUN Chang-Zhi, GOU Beng-Zhang. Attribute Reduction Algorithm on Rough Set and Information Entropy and Its Application[J]. Computer Engineering, 2011, 37(7): 56-58,61.
吴尚智, 苟平章. 粗糙集和信息熵的属性约简算法及其应用[J]. 计算机工程, 2011, 37(7): 56-58,61.