Optimal ROC Curve for a Combination of Classifiers
Marco Antonio Barreno, Alvaro Cardenas and Doug Tygar
We present a new analysis for the combination of binary classifiers. Our analysis makes use of Neyman-Pearson theory as a theoretical basis 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 show how our method generalizes and improves previous work on combining classifiers and generating ROC curves.