Title :
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
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;
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
DOI :
10.1109/ISKE.2008.4730953
