SIA OpenIR  > 工业控制网络与系统研究室
A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy
Wang GD(王国栋)1,2,3,4; Sun LX(孙兰香)1,2,3; Wang W(汪为)1,2,3,4; Chen T(陈彤)1,2,3,4; Guo MT(郭美亭)1,2,3; Zhang P(张鹏)1,2,3
Department工业控制网络与系统研究室
Source PublicationPLASMA SCIENCE & TECHNOLOGY
ISSN1009-0630
2020
Volume22Issue:7Pages:1-10
Indexed BySCI ; EI ; CSCD
EI Accession number20202008655032
WOS IDWOS:000521366500001
CSCD IDCSCD:6774003
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China [2016YFF0102502] ; Key Research Program of Frontier Sciences, CAS [QYZDJ-SSW-JSC037] ; Youth Innovation Promotion Association, CAS, LiaoNing Revitalization Talents Program [XLYC1807110]
Keywordlaser-induced breakdown spectroscopy feature selection ridge regression recursive feature elimination quantitative analysis
Abstract

In the spectral analysis of laser-induced breakdown spectroscopy, abundant characteristic spectral lines and severe interference information exist simultaneously in the original spectral data. Here, a feature selection method called recursive feature elimination based on ridge regression (Ridge-RFE) for the original spectral data is recommended to make full use of the valid information of spectra. In the Ridge-RFE method, the absolute value of the ridge regression coefficient was used as a criterion to screen spectral characteristic, the feature with the absolute value of minimum weight in the input subset features was removed by recursive feature elimination (RFE), and the selected features were used as inputs of the partial least squares regression (PLS) model. The Ridge-RFE method based PLS model was used to measure the Fe, Si, Mg, Cu, Zn and Mn for 51 aluminum alloy samples, and the results showed that the root mean square error of prediction decreased greatly compared to the PLS model with full spectrum as input. The overall results demonstrate that the Ridge-RFE method is more efficient to extract the redundant features, make PLS model for better quantitative analysis results and improve model generalization ability.

Language英语
WOS SubjectPhysics, Fluids & Plasmas
WOS KeywordVARIABLE SELECTION ; LIBS
WOS Research AreaPhysics
Funding ProjectNational Key Research and Development Program of China[2016YFF0102502] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC037] ; Youth Innovation Promotion Association, CAS, LiaoNing Revitalization Talents Program[XLYC1807110]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26644
Collection工业控制网络与系统研究室
Corresponding AuthorSun LX(孙兰香)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Wang GD,Sun LX,Wang W,et al. A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy[J]. PLASMA SCIENCE & TECHNOLOGY,2020,22(7):1-10.
APA Wang GD,Sun LX,Wang W,Chen T,Guo MT,&Zhang P.(2020).A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy.PLASMA SCIENCE & TECHNOLOGY,22(7),1-10.
MLA Wang GD,et al."A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy".PLASMA SCIENCE & TECHNOLOGY 22.7(2020):1-10.
Files in This Item:
File Name/Size DocType Version Access License
A feature selection (1500KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang GD(王国栋)]'s Articles
[Sun LX(孙兰香)]'s Articles
[Wang W(汪为)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang GD(王国栋)]'s Articles
[Sun LX(孙兰香)]'s Articles
[Wang W(汪为)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang GD(王国栋)]'s Articles
[Sun LX(孙兰香)]'s Articles
[Wang W(汪为)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.