Optimal ROC Curve for a Combination of Classifiers
Abstract
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.
Cite
Text
Barreno et al. "Optimal ROC Curve for a Combination of Classifiers." Neural Information Processing Systems, 2007.Markdown
[Barreno et al. "Optimal ROC Curve for a Combination of Classifiers." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/barreno2007neurips-optimal/)BibTeX
@inproceedings{barreno2007neurips-optimal,
title = {{Optimal ROC Curve for a Combination of Classifiers}},
author = {Barreno, Marco and Cardenas, Alvaro and Tygar, J. D.},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {57-64},
url = {https://mlanthology.org/neurips/2007/barreno2007neurips-optimal/}
}