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.
['Hongjian Fan', 'Ming Fan', 'Kotagiri Ramamohanarao', 'Mengxu Liu']
Further improving emerging pattern based classifiers via bagging