通过相互信息最小化,通过公平的生存时间预测
Hyungrok Do1, Yuxin Chang2, Yoon Sang Cho1
1Department of Population Health NYU Grossman School of Medicine.
概括
这项研究引入了公平生存分析的新框架,将预测和敏感属性之间的信息最小化. 该方法提高了预测时间到事件结果的公平性,即使使用了受审查的数据.
科学领域:
- 计算机科学 计算机科学
- 统计 统计 统计 统计
- 机器学习 机器学习
背景情况:
- 生存分析对于预测随时间推移的事件发生至关重要,特别是在审查数据的情况下.
- 算法公平性已经取得了显著的进步,但生存分析中的公平性仍未得到充分探索.
- 现有的方法缺乏可靠的方法,以确保时间到事件预测的公平性.
研究的目的:
- 提出一个新的框架,在生存分析中实现人口平等.
- 为了最大限度地减少预测时间到事件和敏感属性之间的相互信息.
- 为生存预测开发新的差异评估指标.
主要方法:
- 开发了一个框架,以尽量减少生存预测和敏感属性之间的相互信息.
- 实施技术以确保时间到事件预测的统计独立性.
- 提出了为生存分析量身定制的四种新的差异评估指标.
主要成果:
- 拟议的方法有效地减少了相互信息,促进了公平.
- 实验表明,生存预测的公平性有系统地得到改善.
- 这种方法很强大,即使在基准数据集中使用审查数据,效果也很好.
结论:
- 引入的框架成功地提高了生存分析模型的公平性.
- 尽量减少对敏感属性的依赖导致更公平的时间到事件预测.
- 这项工作为开发公平可靠的生存分析系统提供了宝贵的工具.
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