A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory

被引：71

作者：

Zhang, M ^{[1
]}

Xu, LD

Zhang, WX

Li, HZ

机构：

[1] Xi An Jiao Tong Univ, Grad Sch, Xian 710049, Peoples R China

[2] Xi An Jiao Tong Univ, Natl Key Lab Mfg Syst Engn, Xian 710049, Peoples R China

[3] Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China

[4] Old Dominion Univ, Dept Informat Technol & Decis Sci, Norfolk, VA 23529 USA

来源：

EXPERT SYSTEMS | 2003年 / 20卷 / 05期

关键词：

intelligent systems; rough sets; database and knowledge base systems; data mining; machine learning; inclusion degree;

D O I：

10.1111/1468-0394.00254

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The theory of rough sets is an extension of set theory for studying intelligent systems characterized by insufficient and incomplete information. We discuss the basic concept and properties of knowledge reduction based on inclusion degree and evidence reasoning theory, and propose a knowledge discovery approach based on inclusion degree and evidence reasoning theory.

引用

页码：298 / 304

页数：7

共 17 条

[1]

[Anonymous], 2001, THEORY METHODS ROUGH

[2] Rough computational methods for information systems [J].