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#positive-unlabeled

1 curated events
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.

auc/positive-unlabeled/imbalanced-data/research