AUC Maximizing Support Vector Learning

Ulf Brefeld; Tobias Scheffer

AUC Maximizing Support Vector Learning

Ulf Brefeld
, Tobias Scheffer

Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › Begutachtung

Abstract

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

Originalsprache	Englisch
Titel	ROC Analysis in Machine Learning
Seitenumfang	8
Erscheinungsdatum	2005
Publikationsstatus	Erschienen - 2005
Extern publiziert	Ja
Veranstaltung	ICML Worskshop - Bonn, Deutschland Dauer: 11.08.2005 → 11.08.2005 Konferenznummer: 22

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KW - Informatics

KW - Business informatics

M3 - Article in conference proceedings

BT - ROC Analysis in Machine Learning

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AUC Maximizing Support Vector Learning

Abstract

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