Further improving emerging pattern based classifiers via bagging

Authors:
Hongjian Fan;Ming Fan;Kotagiri Ramamohanarao;Mengxu Liu
Affiliations:
Department of CSSE, The University of Melbourne, Parkville, Vic, Australia;Department of Computer Science, Zhengzhou University, Zhengzhou, China;Department of CSSE, The University of Melbourne, Parkville, Vic, Australia;Department of Computer Science, Zhengzhou University, Zhengzhou, China
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

Emerging Patterns (EPs) are those itemsets whose supports in one class are significantly higher than their supports in the other class. In this paper we investigate how to “bag” EP-based classifiers to build effective ensembles. We design a new scoring function based on growth rates to increase the diversity of individual classifiers and an effective scheme to combine the power of ensemble members. The experimental results confirm that our method of “bagging” EP-based classifiers can produce a more accurate and noise tolerant classifier ensemble.