A study on reduction of attributes based on variable precision rough set and information entropy

被引：0
|
作者：
Sun, Ling [1 ]
Chi, Jia-Yu [2 ]
Li, Zhong-Fei [1 ]
机构：
[1] Sun Yat Sen Univ, Coll Lingnan, Guangzhou 510275, Peoples R China
[2] Sun Yat Sen Univ, Sch Management, Guangzhou 510275, Guangdong, Peoples R China
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
基金：
中国国家自然科学基金;
关键词：
variable precision rough set; reduction; information entropy;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
As a powerful tool for inducing classification knowledge from databases, rough set theory can be used to reduce attributes without the requirement of external information. In previous research, the approximation quality gamma is usually used as a criterion in rough set based reduction. But the gamma criterion is of limited value when the relationship between attributes is disturbed by noise. Inspired by previous research, this paper proposes an improved criterion for the reduction of attributes based on variable precision rough set and information entropy. Compared with the gamma criterion, this criterion could gain more tolerance of inconsistency, randomness and noise. A coefficient of correlation for this criterion indicated by epsilon is also proposed in order to make the evaluation more reasonable.
引用
收藏
页码：1412 / +
页数：2
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