English(EN) Using Set Learning to Improve Set-Valued Classifiers on Long-tailed Data

新损失函数改进了AI集合值分类器在不平衡数据上的性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一种odd-K-out (OKO) 损失函数的修改版本，以提高集合值分类器在不平衡数据集上的性能。与标准的交叉熵训练相比，这种改进的OKO损失函数提高了样本效率并降低了计算开销。实验表明，该方法在不显著增加预测集大小的情况下，显著提高了各种集合值分类器在少数类别上的宏观准确率。 AI

影响这项研究可能带来更强大的AI模型，能够处理现实世界中的数据不平衡问题，从而提高各种应用的公平性和准确性。

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arXiv cs.LG TIER_1 English(EN) · Kevin Kasa, Scott C. Lowe, Graham W. Taylor · 2026-10-21 04:00

利用集合学习改进长尾数据集上的值集分类器

arXiv:2610.27443v1 Announce Type: new Abstract: Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets per input rather than fixed-sized sets (e.g. top-1 or top-5). However, set-valued …