An Improved ID3 Decision Tree Algorithm

被引：0
|
作者：
Jin, Chen [1 ]
De-lin, Luo [1 ]
Fen-xiang, Mu [2 ]
机构：
[1] Xiamen Univ, Sch Informat Sci & Technol, Xiamen 361005, Peoples R China
[2] Tsingtao Huanghai Vocat Coll, Qingdao 266427, Peoples R China
来源：
ICCSSE 2009: PROCEEDINGS OF 2009 4TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION | 2009年
基金：
中国国家自然科学基金;
关键词：
data mining; decision tree; ID3; association function(AF); variety bias;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. 1133 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3's inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function(AF) is presented. The experiment results show that the propose algorithm can overcome ID3's shortcoming effectively and get more reasonable and effective rules
引用
收藏
页码：127 / +
页数：2
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