A DEA approach for model combination

Authors:
Zhiqiang Zheng;Balaji Padmanabhan;Haoqiang Zheng
Affiliations:
University of California, Riverside, CA;University of Pennsylvania, Philadelphia, PA;Columbia University, New York, NY
Venue:
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2004

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Abstract

This paper proposes a novel Data Envelopment Analysis (DEA) based approach for model combination. We first prove that for the 2-class classification problems DEA models identify the same convex hull as the popular ROC analysis used for model combination. For general k-class classifiers, we then develop a DEA-based method to combine multiple classifiers. Experiments show that the method outperforms other benchmark methods and suggest that DEA can be a promising tool for model combination.