解决包装偏差问题,并加强回归模型
Juliette Ugirumurera1, Erik A Bensen2, Joseph Severino3
1Computational Science Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA. jugirumu@nrel.gov.
Scientific reports
|August 8, 2024
概括
本研究引入了一种新方法来减少机器学习 (ML) 回归模型中的偏差,显著提高了人工智能应用中少数群体的公平性. 这种方法有效地减轻了树基模型中的偏差超过50%.
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:
- 越来越多的人工智能使用需要在ML模型中进行偏差调查.
- 之前的研究集中在分类上,忽视了回归模型偏差.
- 对于许多应用来说至关重要的回归模型经常表现出性能差异.
研究的目的:
- 提出一种新的,可访问的方法来缓解回归模型中的偏差.
- 将偏差缓解技术扩展到包装和增强组合方法.
- 严格测量和减少与受保护属性相关的偏见.
主要方法:
- 开发了一种偏差缓解技术,适用于最小化可微分损失函数的模型.
- 在模型的损失函数中集成了一个规范化术语,以惩罚与受保护属性的错误相关性.
- 在随机森林,梯度增强树木和XGBoost模型上应用和验证了该方法.
主要成果:
- 提出的方法有效地减少了基于树的集合回归模型中的偏差.
- 预测道路交通量模型中的偏差在少数民族居住地区减少了50%以上.
- 尽管总体准确度很高,但基线模型在少数民族地区的道路上表现不佳.
结论:
- 提出的方法提供了一种有效的解决方案,以减轻回归ML模型中的偏差.
- 这种方法在不同的人口群体中提高了公平和公平的表现.
- 该技术广泛适用于通过可差分损失最小化训练的各种ML模型.
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