FUZZY CONDITIONAL ENTROPY BASED FEATURE EVALUATION AND SELECTION

被引：0
|
作者：
Zhao, Hua [1 ]
Qin, Keyun [1 ]
机构：
[1] Southwest JiaoTong Univ, Sch Math, Chengdu 610031, Sichuan, Peoples R China
来源：
DECISION MAKING AND SOFT COMPUTING | 2014年 / 9卷
基金：
中国国家自然科学基金;
关键词：
Feature selectior; Kernel fuzzy similarity relation; Conditional entropy; delta-attribute reduction; Mixed features; ROUGH SETS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Rough set is an useful tool to characterize uncertain information. It has been widely applied to feature selection. In this paper, we extend the information entropy into the kernel fuzzy rough set. Then, we construct the fast feature selection algorithm based on conditional entropy. Experimental results show that the conditional entropy-based feature selection algorithm can improve the classification performance with few features in most of the cases.
引用
收藏
页码：137 / 142
页数：6
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