从有偏见的选择性标签到伪标签:从有偏见的决策中学习的期望最大化框架
1Division of Computer Science & Engineering, University of Michigan, Ann Arbor, MI, USA.
概括
这项研究引入了一种新的算法,即差异审查期望-最大化 (DCEM),以解决机器学习中的标签偏差. 在不影响模型性能的情况下,DCEM有效地减轻了临床数据中的偏差.
科学领域:
- 机器学习 机器学习
- 因果推理因果推理
- 生物统计学 生物统计学
背景情况:
- 选择性标签源于决策过程,比如诊断测试.
- 标签偏见在各个子组之间可能有所不同,导致不公平地将未标记的数据归因为"负面".
- 标准机器学习模型可能会放大这些现有的标签偏见.
研究的目的:
- 为了解决在机器学习中的不同审查的问题.
- 开发和验证一个算法,减轻不同的审查偏见.
- 提高在偏见的临床数据上训练的机器学习模型的公平性和准确性.
主要方法:
- 提出差异审查期望-最大化 (DCEM),这是一个由选择性标签的因果模型启发的算法.
- 从理论上分析DCEM减轻不同审查效应的机制.
- 使用合成数据集和现实世界的败血症分类任务验证DCEM.
主要成果:
- DCEM显示了改进的偏差缓解,以ROC曲线之间的面积来衡量.
- 与基线方法相比,该算法保持了高分辨性性能 (AUC).
- 在使用临床数据的败血症分类任务中观察到类似的积极结果.
结论:
- DCEM是一种有效的方法,可以从数据中学习,并且有不同的审查.
- 拟议的算法成功地减轻了机器学习模型中的标签偏差放大.
- DCEM为开发更公平,更可靠的医疗保健人工智能提供了一个有希望的方法.
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