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 -