弱监督的AUC优化:一个统一的部分AUC方法
IEEE transactions on pattern analysis and machine intelligence
|January 24, 2024
概括
本研究介绍了WSAUC,这是一个用于优化弱监管的曲线下面面积 (AUC) 的框架. 它使用反向部分AUC (rpAUC) 来有效处理机器学习任务中的不准确标签.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 现实世界的机器学习经常面临不完美的数据,称为弱监督,包括杂的,不完整的或不准确的标签.
- 传统方法与这些数据缺陷作斗争,阻碍了可靠的性能评估和优化.
研究的目的:
- 开发一个统一的框架,WSAUC,以解决在各种弱监管场景下优化曲线下的面积 (AUC).
- 为了提供一个强大的和一致的方法来最大限度地提高AUC,尽管数据受到污染.
主要方法:
- 在各种弱监督设置中,将AUC优化定义为最小化受污染数据集上的AUC风险.
- 引入反向部分AUC (rpAUC) 作为一个新的,强大的培训目标,以最大化AUC.
- 开发WSAUC框架以最大限度地提高经验性rpAUC,使其具有普遍适用性.
主要成果:
- 在弱监督下,经验风险最小化和真实AUC之间的一致性得到证明.
- 经验验证WSAUC在噪音标签,积极无标签,多实例和半监督学习中的有效性.
- 支持WSAUC在多个弱监督的AUC优化任务上的表现的理论和实验证据.
结论:
- 在缺乏监督的情况下,WSAUC为AUC优化提供了通用和有效的解决方案.
- 拟议的rpAUC指标为带有污染标签的培训模型提供了一个强大的目标.
- 该框架成功地将各种弱监管场景集成到一个共同的AUC优化方法中.
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