AUC Maximizing Support Vector Learning
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
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
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 language | English |
|---|---|
| Title of host publication | ROC Analysis in Machine Learning |
| Number of pages | 8 |
| Publication date | 2005 |
| Publication status | Published - 2005 |
| Externally published | Yes |
| Event | International Conference on Machine Learning - Bonn, Germany Duration: 11.08.2005 → 11.08.2005 Conference number: 22 |
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