English(EN) Bayes-Optimal BER and AUC: Estimation and Evaluation of Estimators

用于贝叶斯最优BER和AUC的二元分类新估计量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了用于估计二元分类任务中贝叶斯最优平衡错误率（BER）和ROC曲线下面积（AUC）的新方法。这些估计量即使在软标签被损坏且类别先验未知的情况下也能工作，利用等渗回归来近似干净的软标签。所提出的框架还包括一个评估程序，扩展了FeeBee系统，用于在不知道真实最优值的情况下评估这些估计量在真实数据集上的性能。 AI

影响为评估模型性能提供了高级工具，尤其是在不平衡数据集方面，有助于更稳健的机器学习开发。

排序理由详细介绍分类指标新估计和评估方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Signal score: 53 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 详细介绍分类指标新估计和评估方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Ryota Ushio, Takashi Ishida, Masashi Sugiyama · 2026-09-03 04:00

Bayes-最优 BER 和 AUC：估计量估计与评估

arXiv:2609.02304v1 Announce Type: cross Abstract: A fundamental quantity in machine learning is the optimal performance achievable by any model on a given task. Estimating this quantity allows us to distinguish the irreducible part of the error from a deficiency of the model, tel…

本文如何被排名

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Bayes-最优 BER 和 AUC：估计量估计与评估

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