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New method maximizes pAUC using positive-unlabeled data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for maximizing the partial area under the receiver operating characteristic curve (pAUC) using only positive and unlabeled data. This approach addresses the common challenge where labeled negative data is scarce or difficult to obtain in real-world applications like cybersecurity and medical care. The proposed method, framed within empirical risk minimization, represents pAUC using positive and marginal densities, deriving an empirical estimator from PU data to train classifiers. AI

IMPACT This research could improve classifier performance in domains where negative data is scarce, potentially enhancing applications in cybersecurity and medical diagnostics.

RANK_REASON Academic paper detailing a new method for pAUC maximization from PU data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method maximizes pAUC using positive-unlabeled data

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Academic paper detailing a new method for pAUC maximization from PU data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama, Hiroshi Takahashi, Kazuki Adachi, Yasuhiro Fujiwara ·

    Partial AUC Maximization from Positive-unlabeled Data

    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). Cla…

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