A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling
We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spe...
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| Vydáno v: | Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Ročník 246; s. 118986 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
05.02.2021
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| ISSN: | 1386-1425, 1873-3557, 1873-3557 |
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| Abstract | We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms—BOSS, VCPA, iVISSA and IRF—are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/75828-a-variable-selection-method.
[Display omitted]
•CMW strategy can automatically select the appropriate number and width of interval.•VDPSO algorithm improve the PSO algorithm and reduces the risk of overfitting.•The application of the algorithm VDPSO-CMW in NIR spectral analysis is verified. |
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| AbstractList | We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms-BOSS, VCPA, iVISSA and IRF-are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/75828-a-variable-selection-method.We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms-BOSS, VCPA, iVISSA and IRF-are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/75828-a-variable-selection-method. We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms—BOSS, VCPA, iVISSA and IRF—are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/75828-a-variable-selection-method. [Display omitted] •CMW strategy can automatically select the appropriate number and width of interval.•VDPSO algorithm improve the PSO algorithm and reduces the risk of overfitting.•The application of the algorithm VDPSO-CMW in NIR spectral analysis is verified. |
| ArticleNumber | 118986 |
| Author | Fan, Shuang Xu, Zhuopin Wang, Qi Wang, Haiping Zhang, Pengfei Cheng, Weimin Wu, Yuejin |
| Author_xml | – sequence: 1 givenname: Pengfei surname: Zhang fullname: Zhang, Pengfei organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China – sequence: 2 givenname: Zhuopin surname: Xu fullname: Xu, Zhuopin organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China – sequence: 3 givenname: Qi surname: Wang fullname: Wang, Qi email: wangqi@ipp.ac.cn organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China – sequence: 4 givenname: Shuang surname: Fan fullname: Fan, Shuang organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China – sequence: 5 givenname: Weimin surname: Cheng fullname: Cheng, Weimin organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China – sequence: 6 givenname: Haiping surname: Wang fullname: Wang, Haiping organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China – sequence: 7 givenname: Yuejin surname: Wu fullname: Wu, Yuejin email: yjwu@ipp.ac.cn organization: Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China |
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| Keywords | Intelligent optimization algorithm Particle swarm optimization Multivariate calibration Variable selection Near-infrared spectroscopy |
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| SubjectTerms | Intelligent optimization algorithm Multivariate calibration Near-infrared spectroscopy Particle swarm optimization Variable selection |
| Title | A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling |
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