A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy
文献类型:期刊论文
作者 | 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 |
刊名 | PLASMA SCIENCE & TECHNOLOGY |
出版日期 | 2020 |
卷号 | 22期号:7页码:1-10 |
ISSN号 | 1009-0630 |
关键词 | laser-induced breakdown spectroscopy feature selection ridge regression recursive feature elimination quantitative analysis |
产权排序 | 1 |
英文摘要 | 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. |
WOS关键词 | VARIABLE SELECTION ; LIBS |
资助项目 | National 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] |
WOS研究方向 | Physics |
语种 | 英语 |
CSCD记录号 | CSCD:6774003 |
WOS记录号 | WOS:000521366500001 |
资助机构 | National 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] |
源URL | [http://ir.sia.cn/handle/173321/26644] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Sun LX(孙兰香) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.University of Chinese Academy of Sciences, Beijing 100049, China |
推荐引用方式 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. |
入库方式: OAI收割
来源:沈阳自动化研究所
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