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A novel stacked regression algorithm based on slice transform for small sample size problem in spectroscopic analysis
Yifan Wu1,2; Silong Peng1,2; Qiong Xie1; Quanjie Han1
2018-05
Conference NameICISCE 2018 : IEEE 5th International Conference on Information Science and Control Engineering
Conference DateJuly 20-22, 2018
Conference PlaceZhengzhou, China
Author of SourceHenan University of Science and Technology
Publication PlaceAmerica
PublisherIEEE
Abstract

In spectroscopic data analysis, small sample size (SSS) problem occurs. A solution is to perform variable selection, which has been proved to be critical to improve the performance of the regression model, such as partial least squares (PLS) regression. Stacked moving window partial least squares (SMWPLS) aims to combine variable sets instead of selecting a subset to improve the model robustness. In this study, we proposed a novel weighting strategy to calculate the combination weights. Slice transform (SLT) is used to map the cross-validation (CV) weights to new weights in a piecewise linear manner. The parameters of SLT are optimized with the least-square criterion. Experiments on two near-infrared (NIR) data sets demonstrated the efficiency of the proposed SLT weighting.

KeywordSmall Sample Size Problem Variable Selection Stacked Regression Slice Transform
MOST Discipline Catalogue工学
DOIDOI 10.1109/ICISCE.2018.00026
URL查看原文
Indexed ByEI
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23537
Collection中国科学院自动化研究所
智能制造技术与系统研究中心_多维数据分析
Corresponding AuthorSilong Peng
Affiliation1.Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
2.University of Chinese Academy of Sciences, 100190, Beijing, China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yifan Wu,Silong Peng,Qiong Xie,et al. A novel stacked regression algorithm based on slice transform for small sample size problem in spectroscopic analysis[C]//Henan University of Science and Technology. America:IEEE,2018.
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