监督的分密化用于整理分类模型
James A Jordan1, Caelin P Celani2, Michael Ketterer3
1United States Geological Survey, Reston, VA, USA.
The Analyst
|November 2, 2023
概括
监督离散有效地解散化学传感器数据,优于外部参数整合 (EPO) 以提高分类准确性. 这种新的方法减少了模型的复杂性,并提高了各种化学传感应用中的数据分析.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 化学传感中的多变量分类模型经常受到类内的非信息变异的阻碍.
- 现有的清理方法旨在减少类内差异,同时保持类间差异,以提高模型性能.
- 外部参数对称 (EPO) 是当前数据清理的最先进技术.
研究的目的:
- 引入和展示监督离散作为一种新的方法来解散多变量分类数据.
- 为了比较监督秘密化与已建立的EPO方法的有效性.
- 在现实世界化学传感器应用中评估监督离散的性能.
主要方法:
- 开发了监督分密化,并应用于多变量化学传感器数据.
- 该方法与使用关键性能指标的外部参数整合 (EPO) 进行了比较.
- 该方法在三个不同的分类任务中得到验证:松灰的X射线光 (XRF),手工玻璃的激光诱导分解光谱 (LIBS) 和硬木物种的LIBS.
主要成果:
- 与EPO相比,监督离散显示出更高的清理性能.
- 这种方法导致了更节的模型,参数更少,减少了过度装配的风险.
- 最小化了信息丢失,从而提高了测试应用程序的分类准确性.
结论:
- 监督离散提供了一种比目前的方法更有效和更强大的方法来清理化学传感器数据.
- 这种技术在提高化学传感中多变量分类的性能和可靠性方面具有显著的前景.
- 监督分离的节性质使其成为开发先进化学测量模型的宝贵工具.
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