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Near-Infrared Spectroscopy Analytical Model Using Ensemble Partial Least Squares Regression

文献类型： 外文期刊

作者： Luo, Na 1 ; Han, Ping 2 ; Wang, Shifang 2 ; Wang, Dong 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Liaoning, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Beijing, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

关键词： Ensemble learning; ensemble partial least squares regression coefficient (EPRC); feature selection; near-infrared spectroscopy (NIRS); partial least squares (PLS)

期刊名称：ANALYTICAL LETTERS （ 影响因子：2.329； 五年影响因子：1.738 ）

ISSN： 0003-2719

年卷期： 2019 年 52 卷 11 期

页码：

收录情况： SCI

摘要： A novel ensemble-based feature selection method was developed which is designated as ensemble partial least squares regression coeffientents (EPRC). It was composed of two steps: generating a series of different single feature selectors and aggregating them to reach a consensus. Specifically, the bootstrap resampling approach was used to generate a diversity of single feature selectors, and the absolute values of the regression coefficients of the partial least squares (PLS) model were used to rank the features. Next, these feature rankings out of single feature selectors were aggregated by the weighted-sum approach. Finally, coupled with the regression model, the features selected by EPRC were evaluated through cross validation and an independent test set. By experiments of constructing the spectroscopy analysis model on three near infrared spectroscopy (NIRS) datasets, it was shown that the EPRC located key wavelengths, gave a promotion to regression performance, and was more stable and interpretable to the domain experts.

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