文献类型：期刊文献

英文题名：A Novel Interpretable Ensemble Learning Method for NIR-Based Rapid Characterization of Petroleum Products

作者：Yu, Huijing[1,2];Li, Yuqiang[1,2];Du, Wenli[1,2,3];Yang, Minglei[1,2];Peng, Xin[1,2];Wang, Xinjie[1,2];Long, Jian[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Qingyuan Innovat Lab, Quanzhou 362801, Peoples R China

年份：2023

卷号：72

外文期刊名：IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

收录：;EI(收录号：20233314569902);WOS:【SCI-EXPANDED(收录号:WOS:001053888300017)】；

基金：& nbsp;This work was supported in part by the National Natural Science Foundation of China through the Basic Science Center Program under Grant 61988101, in part by the Major Program of Qingyuan Innovation Laboratory under Grant 00122002, in part by the National Natural Science Foundation of China under Grant 61973124 and Grant 62203173, in part by the Shanghai Sailing Program under Grant 21YF1409900, and in part by the Fundamental Research Funds for the Central Universities under Grant 222202317006.& nbsp;& nbsp;

语种：英文

外文关键词：Predictive models; Petroleum; Reliability; Ensemble learning; Semantics; Prediction algorithms; Training; interpretable machine learning; near-infrared (NIR) spectroscopy; small sample problem

摘要：Near-infrared (NIR) spectroscopy has become an important analytical tool to perform rapid characterization of petroleum products due to its effectiveness and efficiency. However, NIR analysis is a typical small sample problem, and a unitary regression model under specific assumption often suffers from poor generalization ability during online implementation. Besides, the interpretability of complex machine learning models is relative incompetent to reveal the variable contribution of wavelength variables, which is significant to guarantee the model reliability in practical application. Thus, this article proposes a novel ensemble learning algorithm to improve the poor model generalization ability caused by insufficient sample size and complex unexpected online application situations. Furthermore, the constructed model is interpreted by combining model-agnostic explanation method with spectral structure knowledge, and a novel reliability index (RI) is proposed to ensure model reliability in practice. The prediction performance of the proposed algorithm is verified using two industrial NIR datasets. Experimental results confirm the effectiveness of the proposed ensemble learning algorithm. The model interpretability and model reliability are guaranteed by the proposed interpretation method.