Ensemble Learning of Regional Classifiers

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying new data, we apply a weighted voting among the classifiers that include the data in their regions. We used 10 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed the benchmark methods.