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An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration
In our recent work, Monte Carlo Cross Validation Stacked Regression (MCCVSR) is proposed to achieve automatic optimization of spectral interval selection in multivariate calibration. Though MCCVSR performs well in normal conditions, it is still necessary to improve it for more general applications....
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Hindawi Publishing Corporation
2009
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2696029/ https://www.ncbi.nlm.nih.gov/pubmed/19547705 http://dx.doi.org/10.1155/2009/291820 |
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author | Yu, Xiao-Ping Xu, Lu Yu, Ru-Qin |
author_facet | Yu, Xiao-Ping Xu, Lu Yu, Ru-Qin |
author_sort | Yu, Xiao-Ping |
collection | PubMed |
description | In our recent work, Monte Carlo Cross Validation Stacked Regression (MCCVSR) is proposed to achieve automatic optimization of spectral interval selection in multivariate calibration. Though MCCVSR performs well in normal conditions, it is still necessary to improve it for more general applications. According to the well-known principle of “garbage in, garbage out (GIGO)”, as a precise ensemble method, MCCVSR might be influenced by outlying and very bad submodels. In this paper, a statistical test is designed to exclude the ruinous submodels from the ensemble learning process, therefore, the combination process becomes more reliable. Though completely automated, the proposed method is adjustable according to the nature of the data analyzed, including the size of training samples, resolution of spectra and quantitative potentials of the submodels. The effectiveness of the submodel refining is demonstrated by the investigation of a real standard data. |
format | Text |
id | pubmed-2696029 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-26960292009-06-22 An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration Yu, Xiao-Ping Xu, Lu Yu, Ru-Qin J Autom Methods Manag Chem Research Article In our recent work, Monte Carlo Cross Validation Stacked Regression (MCCVSR) is proposed to achieve automatic optimization of spectral interval selection in multivariate calibration. Though MCCVSR performs well in normal conditions, it is still necessary to improve it for more general applications. According to the well-known principle of “garbage in, garbage out (GIGO)”, as a precise ensemble method, MCCVSR might be influenced by outlying and very bad submodels. In this paper, a statistical test is designed to exclude the ruinous submodels from the ensemble learning process, therefore, the combination process becomes more reliable. Though completely automated, the proposed method is adjustable according to the nature of the data analyzed, including the size of training samples, resolution of spectra and quantitative potentials of the submodels. The effectiveness of the submodel refining is demonstrated by the investigation of a real standard data. Hindawi Publishing Corporation 2009 2009-06-11 /pmc/articles/PMC2696029/ /pubmed/19547705 http://dx.doi.org/10.1155/2009/291820 Text en Copyright © 2009 Xiao-Ping Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yu, Xiao-Ping Xu, Lu Yu, Ru-Qin An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration |
title | An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration |
title_full | An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration |
title_fullStr | An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration |
title_full_unstemmed | An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration |
title_short | An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration |
title_sort | improved ensemble method for completely automatic optimization of spectral interval selection in multivariate calibration |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2696029/ https://www.ncbi.nlm.nih.gov/pubmed/19547705 http://dx.doi.org/10.1155/2009/291820 |
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