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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/8860

Title: An Evolutionary and Attribute-Oriented Ensemble Classifier
Authors: Chien-I Lee;Cheng-Jung Tsai;Chih-Wei Ku
Contributors: 數學系
Date: 2006
Issue Date: 2011-05-10T06:29:20Z
Publisher: Springer Verlag
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.
Relation: Lecture Notes in Computer Science, 3981:1210-1218
Appears in Collections:[數學系] 期刊論文

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