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Focused PU learning from imbalanced data

delete2026-09-21
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OA
AI
E
Elias Zavitsanos *
Γ
Γεώργιος Παλιούρας
DOI：10.1007/s10618-026-01264-1delete
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摘要

摘要

En 中文
我们提出了一种在高度不平衡数据集中从正例和未标记（PU）样本中学习的新方法。许多现实世界问题，如疾病基因识别、目标营销、欺诈检测和推荐系统，由于标注数据有限，难以用机器学习方法解决。通常训练数据包含正例和未标记实例，后者通常由负例主导，但也包括一些正例。虽然PU学习已得到广泛研究，但鲜有方法针对不平衡设置或难以检测的、与负例相似的难分正例。我们的方法采用聚焦的经验风险估计器，结合正例和未标记样本来训练二分类器。经验评估表明，该方法在两种标注机制——完全随机选择正例（SCAR）和随机选择（SAR）下，在不平衡数据集上实现了最先进的性能。除这些受控实验外，我们还展示了该方法在金融错报检测这一现实世界应用中的价值。
Keyword:
PU learning
Imbalanced classification
Weakly supervised learning
Learning with noisy labels
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期刊

Data Mining and Knowledge Discovery 封面图
Data Mining and Knowledge Discovery
IF:
4.3
论文数:
210
被引数:
6.0K

机构

I
Institute of Informatics and Telecommunications
学者数:
9
论文数: 5
被引数: 59
引用论文

引用论文

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