积极无标签的学习与标签分发的调整
IEEE transactions on pattern analysis and machine intelligence
|September 26, 2023
概括
本研究引入了带有标签分布对齐 (PULDA) 的正非标签学习,以解决受过有限数据训练的分类器中的偏见. 普尔达调整了标签分配,消除了负面预测偏差,并提高了模型的可区分性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 积极未标记 (PU) 数据对二进制分类提出了挑战,因为缺乏负标签.
- 现有的PU学习方法往往对负面预测有偏见,限制了它们的有效性.
研究的目的:
- 为PU学习提出一种新的配方,以减轻对负面预测的偏见.
- 引入基于边际的学习框架,PULDA,增强模型的可歧视性和控制负预测比例.
主要方法:
- 开发了一种标签分发对齐配方,以控制全球负面预测的比例.
- 引入了功能边际,以提高分类器的可区分性.
- 集成的类预先估计和一个随机的迷你批量优化算法,并保证了趋同.
主要成果:
- 拟议的标签分发对齐本质上消除了对负面预测的偏见.
- 普尔达框架显示了增强的模型可区分性.
- 经验结果证实了在PU学习场景中提出的方法的有效性.
结论:
- 普尔达通过解决固有的偏见和提高分类性能,为PU学习提供了有效的解决方案.
- 该方法在理论上得到了概括分析的支持,并通过全面的实验在实践中得到了验证.
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