Rule generation based on rough set theory for text classification

被引：0

作者：

Bi, YX ^{[1
]}

Anderson, T ^{[1
]}

McClean, S ^{[1
]}

机构：

[1] Univ Ulster, Fac Informat, Newtownabbey BT37 0QB, Antrim, North Ireland

来源：

RESEARCH AND DEVELOPMENT IN INTELLIGENT SYSTEMS XVII | 2001年

关键词：

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：

In this paper we describe an approach based on rough set techniques for decision rule generation applied to text classification. A minimal discriminating set - a reduct - for the original data set is achieved through analyzing the degree of dependency among attributes. To speed up the search for reducts, the information gain criterion is used to reduce the number of attributes considered and rank the attributes in decreasing order, and heuristic functions are incorporated into a range of rule generation algorithms.

引用

页码：157 / 170

页数：14

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