An evolutionary and attribute-oriented ensemble classifier

Authors:
Chien-I Lee;Cheng-Jung Tsai;Chih-Wei Ku
Affiliations:
Graduate Institute of Computer Science and Information Education, National University of Tainan, Tainan, Taiwan, ROC;Department of Computer & Information Science, National Chiao Tung University, Hsinchu, Taiwan, ROC;Graduate Institute of Computer Science and Information Education, National University of Tainan, Tainan, Taiwan, ROC
Venue:
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part II
Year:
2006

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Abstract

In the research area of decision tree, numerous researchers have been focusing on improving the predictive accuracy. However, obvious improvement can hardly be made until the introduction of the ensemble classifier. In this paper, we propose an Evolutionary Attribute-Oriented Ensemble Classifier (EAOEC) to improve the accuracy of sub-classifiers and at the same time maintain the diversity among them. EAOEC uses the idea of evolution to choose proper attribute subset for the building of every sub-classifier. To avoid the huge computation cost for the evolution, EAOEC uses the gini value gained during the construction of a sub-tree as the evolution basis to build the next sub-tree. Eventually, EAOEC classifier uses uniform weight voting to combine all sub-classifiers and experiments show that EAOEC can efficiently improve the predictive accuracy.