Ensemble Learning of Region Based Classifiers 


Vol. 14,  No. 4, pp. 303-310, Aug.  2007
10.3745/KIPSTB.2007.14.4.303


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  Abstract

In machine learning, the ensemble classifier that is a set of classifiers have been introduced for higher accuracy than individual classifiers. We propose a new ensemble learning method that employs a set of region based classifiers. To show the performance of the proposed method, we compared its performance with that of bagging and boosting, which ard existing ensemble methods. Since the distribution of data can be different in different regions in the feature space, we split the data and generate classifiers based on each region and apply a weighted voting among the classifiers. We used 11 data sets from the UCI Machine Learning Repository to compare the performance of our new ensemble method with that of individual classifiers as well as existing ensemble methods such as bagging and boosting. As a result, we found that our method produced improved performance, particularly when the base learner is Naive Bayes or SVM.

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[IEEE Style]

S. H. Choi, B. W. Lee, J. H. Yang, "Ensemble Learning of Region Based Classifiers," The KIPS Transactions:PartB , vol. 14, no. 4, pp. 303-310, 2007. DOI: 10.3745/KIPSTB.2007.14.4.303.

[ACM Style]

Sung Ha Choi, Byung Woo Lee, and Ji Hoon Yang. 2007. Ensemble Learning of Region Based Classifiers. The KIPS Transactions:PartB , 14, 4, (2007), 303-310. DOI: 10.3745/KIPSTB.2007.14.4.303.

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