在标签噪音下学习,通过几次射击的人在循环中改进
Aaqib Saeed1, Dimitris Spathis2, Jungwoo Oh3
1Eindhoven University of Technology, Eindhoven, The Netherlands. a.saeed@tue.nl.
Scientific reports
|February 5, 2025
概括
通过解决噪音标签,Few-Shot人类在循环中改进 (FHLR) 改进了可穿戴健康数据分析. 这种新的方法提高了模型的稳定性和通用性,在健康传感应用中取得了最先进的结果.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 可穿戴设备不断收集健康数据,但获得准确的标签是具有挑战性的.
- 标签噪声是可穿戴数据分析中的一个重要问题,因为缺乏固有的视觉线索.
研究的目的:
- 提出一种新的方法,即Few-Shot Human-in-the-Loop Refinement (FHLR),以解决可穿戴数据中的噪音标签学习问题.
- 为了提高训练在杂可穿戴数据上的模型的概括性和稳定性.
主要方法:
- 使用弱标签的种子模型的初始学习.
- 微调种子模型,使用一小组专家校正.
- 结合种子和微调模型,使用加权参数平均值来提高性能.
主要成果:
- 在从噪音标签中学习方面,FHLR显著优于八种基线方法.
- 在对称和不对称的噪声下,实现了最先进的精度改进,达到[公式:参见文本].
- 与以前的方法不同,对标签噪声的增加表现出了特殊的稳定性.
结论:
- FHLR提供了一个强大的解决方案,用于通过可穿戴技术在健康感知中学习噪音标签.
- 该方法实现了卓越的概括性和稳定性,优于现有技术.
- 提供了有关标签噪声对医疗保健机器学习模型的影响的见解.
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