Decision tree construction based on rough set theory under characteristic relation

被引：0
|
作者：
Song, Jing [1 ]
Li, Tianrui
Wang, Ying [1 ]
Qi, Jianhuai [1 ]
机构：
[1] SW Jiaotong Univ, Res Ctr Secure Applicat Networks & Commun, Chengdu 610031, Peoples R China
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007) | 2007年
关键词：
rough set; decision tree; weighted mean roughness; characteristic relation;
D O I：
10.2991/iske.2007.249
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Several approaches based on rough set have been proposed for constructing decision tree in complete information systems. In fact, many information systems are incomplete in practical applications. In this paper, a new algorithm, Decision Tree Construction based on Rough Set Theory under Characteristic Relation (DTCRSCR), is proposed for mining classification knowledge from incomplete information systems. Its idea is that the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as current splitting node. Experimental results show the decision trees constructed by DTCRSCR tend to have simpler structures and higher classification accuracy.
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页数：1
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