The research and realization of decision trees based on credibility

Author

Li, Fa-chao ; Li, Ping

Author_Institution

Sch. of Econ. & Manage., Hebei Univ. of Sci. & Technol., Shijiazhuang

Volume

1

fYear

2008

fDate

12-15 July 2008

Firstpage

345

Lastpage

349

Abstract

In the inductive learning, if example bank has noise, it is hard to obtain decision trees with high precision, that is, it is hard to obtain knowledge with high credibility. So, this paper puts forward C-ID3 algorithm, by which we compute confidence level of example bank firstly, and further get a kind of decision tree on the basis of traditional ID3 and the credibility obtained. All the theory analysis and simulation indicate that this method posses interesting feature of strong operability, and it also can improve the reliability of knowledge obtained.

Keywords

data mining; decision trees; learning by example; C-ID3 algorithm; confidence level; credibility; data mining; decision trees; inductive learning; knowledge reliability; Costs; Cybernetics; Data mining; Data processing; Databases; Decision trees; Large-scale systems; Machine learning; Probability; Statistics; C-ID3; Credibility; Data mining; Decision tree; ID3; Inductive learning; Nodes purity; Noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2008 International Conference on

Conference_Location

Kunming

Print_ISBN

978-1-4244-2095-7

Electronic_ISBN

978-1-4244-2096-4

Type

conf

DOI

10.1109/ICMLC.2008.4620429

Filename

4620429