Structure-Based Attribute Reduction in Variable Precision Rough Set Models

被引：7

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

页码：657 / 665

页数：9

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