Structure-Based Attribute Reduction in Variable Precision Rough Set Models

被引：6
|
作者：
Inuiguchi, Masahiro [1 ]
机构：
[1] Osaka Univ, Dept Syst Innovat, Grad Sch Engn Sci, 1-3 Machikaneyama, Toyonaka, Osaka 5608531, Japan
来源：
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2006年 / 10卷 / 05期
关键词：
variable precision rough set; reduct; lower approximation; upper approximation; boundary region;
D O I：
10.20965/jaciii.2006.p0657
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, structure-enhancing approaches to attribute reduction are proposed. Ten kinds of meaningful reducts are defined. The relations among them are clarified. Moreover their relations to reducts by structure-preserving approaches are also investigated. A few computational approaches to the proposed reducts are briefly described.
引用
收藏
页码：657 / 665
页数：9
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