Optimizing Two-way Partial AUC with an End-to-end Framework. (arXiv:2206.11655v1 [cs.LG])

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning,
which evaluates the average performance over all possible True Positive Rates
(TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful
classifier should simultaneously embrace a high TPR and a low FPR, we turn to
study a more general variant called Two-way Partial AUC (TPAUC), where only the
region with $\mathsf{TPR} \ge \alpha, \mathsf{FPR} \le \beta$ is included in
the area. Moreover, recent …

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