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An ensemble learning algorithm based on generalized attribute value partitioning

The method of disturbing training data randomly to train individual classifiers has been widely applied in some ensemble learning methods such as Bagging and Boosting to achieve strong generalization ability, however, it seems something blind. In this paper, a new ensemble learning algorithm named GAVPEL is proposed. By using the hierarchy nature of the data set, GAVPEL leverages the generalized attribute value partitioning method to form an ensemble tree, called a generalized classifier hierarchy tree. While classifying, GAVPEL selects part of the individual classifiers based on attribute value and ensembles them with majority voting. Experiment results show that GAVPEL can efficiently improve generalization performance when compared with some popular ensemble learning algorithms.