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This paper deals with the combination of classification mod- els that have been derived from running dierent (heterogeneous) learn- ing algorithms on the same data set. We focus on the Classifier Evalua- tion and Selection (ES) method, that evaluates each of the models (typi- cally using 10-fold cross-validation) and selects the best one. We examine the performance of this method in comparison with the Oracle selecting the best classifier for the test set and show that 10-fold cross-validation has problems in detecting the best classifier. We then extend ES by ap- plying a statistical test to the 10-fold accuracies of the models and com- bining through voting the most significant ones. Experimental results show that the proposed method, Eective Voting, performs comparably with the state-of-the-art method of Stacking with Multi-Response Model Trees without the additional computational cost of meta-training.

Effective Voting of Heterogeneous Classifiers