An improved ID3 decision tree algorithm

Author

Jin, Chen ; De-Lin, Luo ; Fen-Xiang, Mu

Author_Institution

Sch. of Inf. Sci. & Technol., Xiamen Univ., Xiamen, China

fYear

2009

fDate

25-28 July 2009

Firstpage

127

Lastpage

130

Abstract

Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 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 proposed algorithm can overcome ID3´s shortcoming effectively and get more reasonable and effective rules.

Keywords

computational complexity; data mining; decision trees; ID3 decision tree algorithm; association function; computational complexity; data mining; model classification; model prediction; Computer networks; Computer science; Computer science education; Data mining; Decision trees; Educational technology; Entropy; Information science; Predictive models; Testing; ID3; association function(AF); data mining; decision tree; variety bias;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science & Education, 2009. ICCSE '09. 4th International Conference on

Conference_Location

Nanning

Print_ISBN

978-1-4244-3520-3

Electronic_ISBN

978-1-4244-3521-0

Type

conf

DOI

10.1109/ICCSE.2009.5228509

Filename

5228509