在高维度,低样本大小数据上进行特征选择的生物目标梯度下降
Tina Issa1, Eric Angel1, Farida Zehraoui1
1Universite Paris-Saclay, Univ Evry, IBISC, Evry-Courcouronnes, France.
PloS one
|July 18, 2024
概括
这项研究引入了一种新的深度学习方法,将特征选择和网络散散化整合在一起. 该方法提高了模型的准确性和稀疏性,在高维,低样本大小的数据挑战中表现优于现有的方法.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 深度学习模型通常在应用到高维和低样本大小 (HDLSS) 数据时过度适应,这在罕见疾病诊断中很常见.
- 传统的解决方案,如功能选择和网络散散化,通常是独立地解决的.
- 这种局限性阻碍了深度学习在医学诊断等关键领域的有效应用.
研究的目的:
- 提出一种新的方法,将特征选择和网络散散化整合到深度神经网络培训过程中.
- 为HDLSS数据开发一种优化网络稀疏性和模型精度之间的权衡的方法.
- 为了提高深度学习模型的性能,在以有限的数据和众多功能为特征的场景中.
主要方法:
- 开发了一种受约束的生物目标梯度下降方法,以同时优化特征选择和网络稀疏性.
- 该方法将特征选择直接集成到深度神经网络的训练中,将其视为分散问题.
- 该方法产生了一组帕雷托最佳神经网络,在稀疏性和准确性之间提供了一系列权衡.
主要成果:
- 综合方法在人工数据集上显著增加了网络稀疏性 (0.92) 和特征选择得分 (0.97),同时保持了高精度 (0.9).
- 与其他方法相比,拟议的方法在相当的准确度水平上实现了更高的特征选择和稀疏度得分.
- 统计验证证实了受约束的生物目标梯度下降方法在人工和现实世界数据集中的有效性.
结论:
- 通过散散化将特征选择集成到深度神经网络训练中,是HDLSS数据的有效策略.
- 受约束的生物目标梯度下降方法成功地平衡了网络稀疏性和分类准确性.
- 这种统一的方法为具有挑战性的数据集提供了相对于单独的特征选择和分散技术的显著进步.
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