Constructing ensembles from data envelopment analysis

Using an ensemble of models often results in better performance than using a single "best" model. We present a new approach based on data envelopment analysis (DEA) for model combination. We prove that for two-class classification problems, DEA models identify the same convex hull as does the popular receiver operating characteristics (ROC) analysis used for model combination. We further develop two DEA-based methods to combine classifiers for the more general k-class classification problems. Our results demonstrate that the two methods outperform other benchmark methods and suggest that DEA can be a powerful toot for model combination.