提高拉曼光谱学分类中的可解释性,使用SHAP和光谱细分
Zhichao Yang1, Liang Meng2, Wenjian Rui3
1Department of Forensic Science, Zhejiang Police College, Hangzhou 310053, PR China.
概括
GRASS是一种新的光谱分类方法,通过分析光谱区域,而不仅仅是点,提高了可解释性. 这种方法与分子振动保持一致,并为法医分析等应用程序提高模型透明度.
科学领域:
- 频谱学是一种光谱学.
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 解释光谱分类模型是一项挑战.
- 传统方法往往侧重于单个数据点,缺少区域光谱信息.
- 需要提供对光谱分类决定有物理意义的解释的方法.
研究的目的:
- 介绍GRASS (梯度区域分析的光谱SHAP),一种新方法来提高光谱分类的解释性.
- 提高拉曼光谱分类的透明度和物理解释性.
- 验证已识别的光谱区域与分子振动模式的物理依据.
主要方法:
- 将梯度区域分析与光谱SHAP值量化进行整合.
- 自动将高维光谱数据分成连续区域,使用反向传播梯度.
- 应用SHAP来量化每个已识别的光谱区域对分类的贡献.
主要成果:
- 格拉斯确定了与分子振动模式相对应的关键光谱区域.
- 比起Grad-CAM和LIME,GRASS表现出与协同作用的光谱区域相互作用的更好对齐.
- 在不同的数据规范化,网络架构和超频谱数据集中,GRASS显示出强度.
结论:
- 格拉斯为光谱分类模型提供了增强的解释性.
- 该方法与物理原理保持一致,提供了具有物理意义的解释.
- 格拉斯适用于高可靠性场,并且可以扩展到其他光谱类型.
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