The data mining method based on second learning

Author

Li, Yan ; Li, Guo-gang ; Li, Fa-chao ; Jin, Chen-xia

Author_Institution

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

Volume

1

fYear

2008

fDate

12-15 July 2008

Firstpage

340

Lastpage

344

Abstract

Decision tree algorithm is not only the important part of machine learning, but also the most widely used data mining tool. At present, there are many algorithms of generating decision tree, but when the database which we rely on exists noise, high quality knowledge is hard to obtain by ID3 algorithm. In this paper, we propose the data mining method based on second learning in case of ID3 algorithm, and analyze the performance of our method by a concrete database. Theory analysis and simulation indicate that this method posses the feature of strong operability, and it can improve the reliability of obtained knowledge.

Keywords

data mining; decision trees; learning (artificial intelligence); data mining method; decision tree algorithm; machine learning; second learning; Algorithm design and analysis; Concrete; Data analysis; Data mining; Decision trees; Machine learning; Machine learning algorithms; Noise generators; Performance analysis; Spatial databases; Accuracy rate; Decision tree; ID3 algorithm; Noise; Second learning;

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.2008.4620428

Filename

4620428