English(EN) PU classification under Non-SCAR: clustering-assisted logistic model with oversampling enhancement

新的PU分类方法整合SMOTE以提高准确性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新的基于逻辑回归的PU分类方法，专门解决SCAR假设的违反问题。该方法整合了SMOTE技术来处理类别不平衡并提高分类性能。在多个基准数据集上的实验表明，所提出的方法，特别是与SMOTE结合的LassoJoint方法，在SCAR条件不满足的情况下显示出更高的准确性和鲁棒性。 AI

影响这项研究可能在具有不平衡数据集和违反假设的领域带来更准确的分类模型。

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arXiv cs.AI TIER_1 English(EN) · Konrad Furma\'nczyk, Kacper Paczutkowski · 2026-09-15 04:00

非SCAR下的PU分类：基于聚类辅助和过采样增强的逻辑回归模型

arXiv:2609.14675v1 Announce Type: cross Abstract: This study addresses the PU classification problem under violations of the SCAR assumption. We investigate logistic regression-based approaches, namely the cluster method and its extensions with strict and non-strict Lasso regular…