基于EKLD的属性约简方法  

Attribute Reduction Method Based on Extended Kullback-Leibler Distance

作　　者：周如旗[1] 陈文伟[2] 

机构地区：[1]广东教育学院计算机科学系,广州510303 [2]海军兵种指挥学院,广州510431

出　　处：《计算机工程》2007年第11期62-63,共2页Computer Engineering

摘　　要：信息的Kullback-Leibler距离能够很好地表示两个分布函数所包含信息的差异程度。文章提出了扩展Kullback-Leibler距离的概念,在此基础上提出了一种新的基于扩展Kullback-Leibler距离的属性约简算法。实验分析表明,在多数情况下该算法能够得到决策表的最小相对约简,同时还对算法复杂度作了简单分析。Kullback-Leibler distance of information can be used to measure the difference degree between the random variables. This paper presents the concept of extended Kullback-Leibler distance. A new reduction algorithm based on extended Kullback-Leibler distance for knowledge is put forward, and the complexity of the algorithm is analyzed simply. The experimental results show that this algorithm can find out the relative minimal reduction for most decision tables.

关 键 词：ROUGH SET理论 信息论 Kullback—Leibler距离 属性约简 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]