Optimal ROC Curve for a Combination of Classifiers

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Marco Barreno, Alvaro Cardenas, J. D. Tygar


We present a new analysis for the combination of binary classifiers. We propose a theoretical framework based on the Neyman-Pearson lemma to analyze combinations of classifiers. In particular, we give a method for finding the optimal decision rule for a combination of classifiers and prove that it has the optimal ROC curve. We also show how our method generalizes and improves on previous work on combining classifiers and generating ROC curves.