A Maximum contribution method for classification based on information theory

Author

Keming, Lin ; Yongsheng, Xue ; Juan, Wen

Author_Institution

Dept. of Math. & Comput. Sci., Sanming Univ., Sanming, China

Volume

1

fYear

2008

fDate

17-19 Nov. 2008

Firstpage

345

Lastpage

350

Abstract

Inductive learning for classification based on information theory is one of the important topics in data mining. We here propose an maximum contribution method for classification based on information theory. According to the theory of channel transmission in information theory, the definition contribution is developed based on probability distribution of classified space, probability transfer matrices of classified space and feature space and mutual information, then entities is classified by the Maximum contribution method. Finally the empirical test and analyses prove the feasibility of the method.

Keywords

data mining; information theory; learning by example; statistical distributions; classification; data mining; inductive learning; information theory; maximum contribution method; probability distribution; probability transfer matrices; Computer science; Data mining; Information entropy; Information theory; Intelligent systems; Knowledge engineering; Learning systems; Mathematics; Probability distribution; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent System and Knowledge Engineering, 2008. ISKE 2008. 3rd International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4244-2196-1

Electronic_ISBN

978-1-4244-2197-8

Type

conf

DOI

10.1109/ISKE.2008.4730953

Filename

4730953