# Support Vector Machines and Area Under ROC curve

@inproceedings{Rakotomamonjy2004SupportVM, title={Support Vector Machines and Area Under ROC curve}, author={Alain Rakotomamonjy}, year={2004} }

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a SVMs based algorithm for AUC maximization and show that under certain conditions this algorithm is related to 2-norm soft…

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