Fuzzy decision tree based on the important degree of fuzzy attribute

被引：0
|
作者：
Wang, Xi-Zhao [1 ]
Zhai, Jun-Hai [1 ]
Zhang, Su-Fang [2 ]
机构：
[1] Hebei Univ, Coll Math & Comp Sci, Key Lab Machine Learning & Computat Intelligence, Baoding 071002, Hebei, Peoples R China
[2] Hebei Informat Engn Sch, Teaching & Res Sect Math, Baoding 071000, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2008年
关键词：
condition attribute; decision attribute; fuzzy attribute; fuzzy decision tree; important degree;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
There may be many fuzzy attributes in a fuzzy information system. Different fuzzy attribute has different contribution to classification. More important attributes have more contribution than the others to decision-making. In this paper, based on the importance of the fuzzy condition attributes, a new method generating a fuzzy decision tree is proposed, which uses the important degree of the fuzzy condition attribute with respect to the fuzzy decision attributes to select attributes to expand the branches of a fuzzy decision tree. A comparison between the new method and fuzzy ID3 is provided. It is shown that the new method is more efficient than fuzzy ID3.
引用
收藏
页码：511 / +
页数：3
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