AUC Maximizing Support Vector Learning
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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AUC Maximizing Support Vector Learning. / Brefeld, Ulf; Scheffer, Tobias.
ROC Analysis in Machine Learning. 2005.Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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TY - CHAP
T1 - AUC Maximizing Support Vector Learning
AU - Brefeld, Ulf
AU - Scheffer, Tobias
N1 - Conference code: 22
PY - 2005
Y1 - 2005
N2 - 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
AB - 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
KW - Informatics
KW - Business informatics
M3 - Article in conference proceedings
BT - ROC Analysis in Machine Learning
T2 - International Conference on Machine Learning
Y2 - 11 August 2005 through 11 August 2005
ER -