一类支持向量机器用于检测部署机器学习中的人口漂移医学诊断.
William S Jones1, Daniel J Farrow2
1Centre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), Faculty of Science and Engineering, University of Hull, Hull, UK. will.jones@hull.ac.uk.
Scientific reports
|April 9, 2025
概括
机器学习 (ML) 模型可以从真实世界的数据中漂移,导致错误. 一个一级支持矢量机器 (OCSVM) 能够有效地检测到这种人口漂移,确保更安全的ML诊断.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 模型对于疾病诊断和预测至关重要.
- 人口漂移,培训与现实数据之间的差异,降低了ML模型的性能,并可能导致诊断错误.
- 现有的漂移检测方法有限,往往缺乏直接测量或需要地面真实标签.
研究的目的:
- 提出和评估一种新的方法来检测ML模型中的人口漂移,使用一类支持向量机器 (OCSVM).
- 评估OCSVM对不同级别模拟数据噪声的敏感性,这表明了人口漂移.
主要方法:
- 在威斯康星州乳腺癌数据集上训练了一类支持向量机器 (OCSVM).
- 模拟数据,控制偏移和噪声水平 (5%,10%,30%),用于测试OCSVM的漂移检测能力.
- 每个噪音级别的OCSVM检测到的入射器数量被记录下来.
主要成果:
- 随着噪音水平的上升,OCSVM检测到越来越多的入口者:27个入口者在5%的噪音下,486个在10%的噪音下,851个在30%的噪音下.
- 这表明增加的数据噪声 (模拟人口漂移) 和由OCSVM识别的入量数量之间存在相关性.
- 结果表明OCSVM对检测数据分布偏差的敏感性.
结论:
- 拟议的OCSVM方法有效地检测了ML模型中的人口漂移.
- 这种方法可以作为一个关键的警报系统,支持在诊断中安全采用ML.
- 未来的工作应该集中在现实世界的数据验证,模型透明度,并探索补充方法.
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