Optimizing Area Under the ROC Curve using Ranking SVMs
نویسندگان
چکیده
Area Under the ROC Curve (AUC), often used for comparing classifiers, is a widely accepted performance measure for ranking instances. Many researches have studied optimization of AUC, usually via optimizing some approximation of a ranking function. Ranking SVMs are among the better performers but their usage in the literature is typically limited to learning a total ranking from partial rankings . In this paper, we observe that a ranking SVM is in fact a direct optimization of AUC via optimizing the Wilcoxon-MannWhitney statistic. We compare a linear ranking SVM with some well-known linear classifiers such as linear SVM, perceptron and logit in the context of binary classification. We show that an SVM optimized for ranking not only achieves better AUC than other linear classifiers on average, but also performs comparably in accuracy.
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