半监督呼吸声分类的联合基于能源的模型:一种对分布不匹配不敏感的方法
IEEE journal of biomedical and health informatics
|October 15, 2024
概括
这项研究引入了一种新型的半监督学习方法,通过使用外部未标记的数据来改善呼吸声的分类. 该方法有效地处理分布不匹配,显著提高分类性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 生物医学信号处理
背景情况:
- 半监督学习 (SSL) 利用未标记的数据来提高模型性能,特别是当标记的数据稀缺时.
- 获取大型未标记的数据集用于呼吸声分类是耗时的,延迟了研究.
- 当前的SSL方法通常会失败,因为未标记的数据来源于与标记数据不同的分布.
研究的目的:
- 开发一种新的半监督方法,能够利用外部未标记的呼吸声数据,即使分布不匹配.
- 通过使用多种数据源来解决当前SSL技术在呼吸声分类中的局限性.
- 提高呼吸声分类任务的效率和减少研究时间表.
主要方法:
- 提出了一种基于联合基于能源的模型 (JEM) 的半监督方法.
- 专注于样本中的基本语义组件的建模,以实现分布的稳定性.
- 使用标记数据集 (ICBHI 2017) 和外部未标记数据集 (HF_Lung_V1,COVID-19声音) 训练模型.
主要成果:
- 提出的基于JEM的方法表明,它对标记数据和外部未标记数据之间的分布不匹配不敏感.
- 成功利用外部未标记的数据来缓解标记数据的稀缺性.
- 与基线方法相比,实现了12.86%的性能改善.
结论:
- 开发的半监督方法有效地利用外部未标记的数据进行呼吸声分类,优于现有的方法.
- 基于JEM的方法提供了一个强大的解决方案,用于在现实世界中处理分销转移.
- 这种方法有可能通过克服数据采集挑战来加速呼吸声分析的研究和开发.
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