In machine learning, ensemble classifiers have been introduced for more accurate pattern classification than single classifiers. We propose a new ensemble learning method that employs a set of region based classifiers. 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 other ensemble methods such as bagging and boosting. As a result, we found that our method improve performance, particularly when the base learner is Na?ve Bayes or SVM.