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papersSEP 12 04:00 UTC

Paper Proposes AUC Maximization from Biased Positive-Unlabeled Data with Confidence

A new arXiv paper addresses AUC maximization for imbalanced binary classification when reliable negative examples are unavailable. The authors tackle the realistic setting of biased positive-unlabeled data and incorporate confidence estimates into the learning procedure. This approach aims to improve ranking performance without requiring clean negative labels.

arXivAUC maximizationPositive-unlabeled learningbiased positive-unlabeled dataconfidence estimationimbalanced binary classification

COVERAGE · 2 REPORTS · LINKS GO TO THE ORIGINAL OUTLETS

arXiv cs.LGAUC Maximization from Biased Positive-unlabeled Data with Confidence ↗SEP 11 04:00 UTC
arXiv cs.AIAUC Maximization from Biased Positive-unlabeled Data with Confidence ↗SEP 12 04:00 UTC

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