Optimal ROC Curve for a Combination of Classifiers
Marco Barreno, Álvaro A. Cárdenas, J. D. Tygar · 2007
We present a new analysis for the combination of binary classifiers. Our analysis makes use of the Neyman-Pearson lemma as a theoretical basis to analyze combi-nations of classifiers. 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 show how our method generalizes and improves previous work on combining classifiers and generating ROC curves. 1