文献类型：期刊文献

英文题名：Near-Infrared Wavelength-Selection Method Based on Joint Mutual Information and Weighted Bootstrap Sampling

作者：Wang, Kai[1,2];Du, Wenli[1,2];Long, Jian[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2020

卷号：16

期号：9

起止页码：5884

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20202408810040);WOS:【SCI-EXPANDED(收录号:WOS:000542966300024)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61973124, (Basic Science Center Program: 61988101), (Major Program: 61590923), (Major Program: 61590922), and in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61725301. Paper no. TII-19-2925.

语种：英文

外文关键词：Mutual information; Random variables; Entropy; Predictive models; Spectroscopy; Indexes; Input variables; Mutual information; near infrared (NIR) model; wavelength selection; weighted bootstrap sampling

摘要：Near-infrared (NIR) spectroscopy is widely used to estimate product quality and other key variables. Eliminating redundant variables is very important in constructing a high-quality NIR model. This article proposes a new wavelength-selection method for NIR spectroscopy based on joint mutual information and weighted bootstrap sampling (WBS). The method considers the combination effect of variables and involves the dynamic selection of wavelength in each iteration to increase the model prediction accuracy. The index based on joint mutual information is used to determine the importance of variables and thus accurately reflects the variable-combination effect. WBS is further used to dynamically adjust the importance of candidate variables, i.e., to increase the weights of samples with poor prediction results and decrease those of samples with accurate prediction. This process ensures that the subsequently selected wavelength focuses on inaccurately estimated samples. The performance of this method is demonstrated through three NIR datasets of gasoline, shootout, and diesel fuels. The proposed method is found to have better accuracy than the traditional partial-least-squares method, variable iterative space shrinkage approach, and several other wavelength-selection methods.