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Abstract: The area under an ROC curve (AUC) is a criterion used in many applications to measure the quality of a classification algorithm. However, the objective function optimized in most of these algorithms is the error rate and not the AUC value. We give a detailed statistical analysis of the relationship between the AUC and the error rate, including the first exact expression of the expected value and the variance of the AUC for a fixed error rate. Our results show that the average AUC is... (Update)
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BibTeX entry: (Update)
C. Cortes and M. Mohri, `AUC optimization vs. error rate minimization ', in Advances in Neural Information Processing Systems 16, eds., Sebastian Thrun, Lawrence Saul, and Bernhard Sch olkopf, MIT Press, Cambridge, MA, (2004). http://citeseer.ist.psu.edu/cortes03auc.html More
@misc{ cortes04auc,
author = "C. Cortes and M. Mohri",
title = "AUC optimization vs. error rate minimization",
text = "C. Cortes and M. Mohri, `AUC optimization vs. error rate minimization ',
in Advances in Neural Information Processing Systems 16, eds., Sebastian
Thrun, Lawrence Saul, and Bernhard Sch olkopf, MIT Press, Cambridge, MA,
(2004).",
year = "2004",
url = "citeseer.ist.psu.edu/cortes03auc.html" }
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