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Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration

文献类型：期刊论文

作者Shan, Peng2; Zhao, Yuhui3; Wang, Qiaoyun2; Sha, Xiaopeng2; Lv, Xiaoyong2; Peng, Silong1; Ying, Yao2
刊名SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
出版日期2019-05-15
卷号215页码:97-111
关键词Extreme learning machine (ELM) Partial least squares (PLS) Nonlinear multivariate calibration Stacked generalization
ISSN号1386-1425
DOI10.1016/j.saa.2019.02.089
通讯作者Zhao, Yuhui(yuhuizhao@neuq.edu.cn)
英文摘要With its simple theory and strong implementation, extreme learning machine (ELM) becomes a competitive single hidden layer feed forward networks for nonlinear multivariate calibration in chemometrics. To improve the generalization and robustness of ELM further, stacked generalization is introduced into ELM to construct a modified ELMmodel called stacked ensemble ELM (SE-ELM). The SE-ELM is to create a set of sub-models by applying ELMrepeatedly to different sub-regions of the spectra and then combine the predictions of those sub-models according to aweighting strategy. Three differentweighting strategies are explored to implement the proposed SE-ELM, such as theWinner-takes-all (WTA) weighting strategy, the constraint non-negative least squares (CNNLS) weighing strategy and the partial least squares (PLS) weighting strategy. Furthermore, PLS is suggested to be selected as the optimal weighting method that can handle the multi-colinearity among the predictions yielded by all the sub-models. The experimental assessment of the three SE-ELM models with differentweighting strategies is carried out on six real spectroscopic datasets and compared with ELM, back-propagation neural network (BPNN) and Radial basis function neural network (RBFNN), statistically tested by the Wilcoxon signed rank test. The obtained experimental results suggest that, in general, all the SE-ELM models are more robust and more accurate than traditional ELM. In particular, the proposed PLS-based weighting strategy is at least statistically not worse than, and frequently better than the other two weighting strategies, BPNN, and RBFNN. (C) 2019 Published by Elsevier B.V.
WOS关键词NEAR-INFRARED SPECTROSCOPY ; NIR SPECTROSCOPY ; REFLECTANCE SPECTROSCOPY ; VARIABLES SELECTION ; NEURAL NETWORKS ; REGRESSION ; VALIDATION ; NITROGEN ; PROTEIN ; DESIGN
资助项目National Natural Science Foundation of China[61601104] ; Natural Science Foundation of Hebei Province[F10017501052] ; Natural Science Foundation of Hebei Province[XNB1001611]
WOS研究方向Spectroscopy
语种英语
WOS记录号WOS:000471316200012
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province
源URL[http://ir.ia.ac.cn/handle/173211/27860]  
专题自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者Zhao, Yuhui
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
3.Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
推荐引用方式
GB/T 7714		 Shan, Peng,Zhao, Yuhui,Wang, Qiaoyun,et al. Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration[J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,2019,215:97-111.
APA Shan, Peng.,Zhao, Yuhui.,Wang, Qiaoyun.,Sha, Xiaopeng.,Lv, Xiaoyong.,...&Ying, Yao.(2019).Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,215,97-111.
MLA Shan, Peng,et al."Stacked ensemble extreme learning machine coupled with Partial Least Squares-based weighting strategy for nonlinear multivariate calibration".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 215(2019):97-111.

入库方式： OAI收割

来源：自动化研究所

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