Constructing Ensembles from Data Envelopment Analysis
Zhiqiang Zheng, Balaji Padmanabhan
INFORMS Journal on Computing · 2007 · Volume 19 · Issue 4
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.
Keywords
model combination, ensembles, data envelopment analysis
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