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通过虚拟样本构建来提高预测模型的稳定性,用于近红外光谱分析

Yong Hao¹, Xiyan Li², Chengxiang Zhang²

¹School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, China; Key Laboratory of Conveyance Equipment of the Ministry of Education, Nanchang, 330013, China.

Analytica chimica acta

|October 12, 2023

概括

虚拟样本生成可以改进近红外光谱学 (NIRS) 模型. 深度卷积生成对抗网络 (DCGAN) 增强了模型的稳定性和概括性,优于其他不平衡光谱数据分析方法.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
化学测量化学测量化学测量

背景情况:

近红外光谱 (NIRS) 模型在处理有限或不平衡的样本数据时,其稳定性和概括性不佳.
样本规模不足或不平衡阻碍了可靠的NIRS分析模型的开发,特别是当样本采集具有挑战性时.
虚拟样本构建是解决数据不平衡和提高NIRS模型性能的一个关键策略.

研究的目的:

评估三个虚拟频谱构建策略:合成少数群体过量采样技术 (SMOTE),自适应合成采样 (ADASYN) 和深度卷积生成对抗网络 (DCGAN).
评估这些策略在提高具有不平衡数据集的NIRS模型的稳定性和概括能力方面的有效性.
使用NIRS光谱数据,比较SMOTE,ADASYN和DCGAN在提高分辨器模型精度方面的性能.

主要方法:

探索SMOTE,ADASYN和DCGAN用于虚拟频谱生成,以平衡光谱数据集.
将这些策略应用于胺和Yali梨光谱数据集.
使用部分最小平方-歧视分析 (PLS-DA) 构建歧视模型,并通过正确识别率 (CRR) 评估准确性.

主要成果:

所有测试的策略 (SMOTE,ADASYN,DCGAN) 都改善了全球正确识别率 (CRR).
SMOTE和ADASYN改善了全球CRR,但降低了少数类样本的CRR (CRR_min).
通过改进CRR_glob,CRR_maj和CRR_min,DCGAN表现出卓越的性能,其结果显示出高强度和可靠性.

结论:

深度卷积生成对立网络 (DCGAN) 是生成虚拟光谱样本的可靠和实用的方法.
DCGAN有效地提高了NIRS模型的稳定性和概括能力,特别是在样本数据不足或不平衡的场景中.
该研究强调DCGAN是克服NIRS分析数据局限性的有希望的方法,优于SMOTE和ADASYN等传统方法.

关键词:

深度卷积的生成对抗网络.近红外光谱学近红外光谱学频谱数据集平衡的方法虚拟样本建设的虚拟样本建设.

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