AUC Maximizing Support Vector Learning

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The area under the ROC curve (AUC) is a natural performance measure when thegoal is to find a discriminative decision function.We present a rigorous derivation of an AUC maximizing Support Vector Machine; its optimization criterion is composed of a convex bound on the AUC and a margin term.
The number of constraints in the optimization problem grows quadratically in the number of examples. We discuss an approximation for large data sets that clusters the constraints. Our experiments show that the AUC maximizing Support Vector Machine does in fact lead to higher AUC values
Original languageEnglish
Title of host publicationROC Analysis in Machine Learning
Number of pages8
Publication date2005
Publication statusPublished - 2005
Externally publishedYes
EventInternational Conference on Machine Learning - Bonn, Germany
Duration: 11.08.200511.08.2005
Conference number: 22

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