Constructing Ensembles from Data Envelopment Analysis

Authors:
Zhiqiang Zheng;Balaji Padmanabhan
Affiliations:
School of Management, University of Texas at Dallas, Richardson, Texas 75083;The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
Venue:
INFORMS Journal on Computing
Year:
2007

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Abstract

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 tool for model combination.