arXiv Artificial Intelligence

Partial AUC Maximization from Positive-unlabeled Data

1 hour ago 02.10.2026 07:00 👁 1 views
Partial AUC Maximization from Positive-unlabeled Data

Quick summary

arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes true positive rates within a specific range of false positive rates (FPRs). Classifiers that achieve high pAUC need to be obtained in many real-world applications such as cybersecurity, medical care, and advertising. Although many methods for maximizing the pAUC have been proposed, they typically require both labeled positive and negative data for training. However, in practice, labeled negative

Key takeaways

  • arXiv:2610.00284v1 Announce Type: cross Abstract: The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes true positive rates within a specific range of false positive rates (FPRs).
  • Classifiers that achieve high pAUC need to be obtained in many real-world applications such as cybersecurity, medical care, and advertising.
  • Although many methods for maximizing the pAUC have been proposed, they typically require both labeled positive and negative data for training.

Why it matters

“Partial AUC Maximization from Positive-unlabeled Data” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗