A hybrid approach for efficient ensembles

An ensemble of classifiers, or a systematic combination of individual classifiers, often results in better classifications in comparison to a single classifier. However, the question regarding what classifiers should be chosen for a given situation to construct an optimal ensemble has often been debated. In addition, ensembles are often computationally expensive since they require the execution of multiple classifiers for a single classification task. To address these problems, we propose a hybrid approach for selecting and combining data mining models to construct ensembles by integrating Data Envelopment Analysis and stacking. Experimental results show the efficiency and effectiveness of the proposed approach.