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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Bibliographic Details
Published in:Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 246; p. 118986
Main Authors: Zhang, Pengfei, Xu, Zhuopin, Wang, Qi, Fan, Shuang, Cheng, Weimin, Wang, Haiping, Wu, Yuejin
Format: Journal Article
Language:English
Published: Elsevier B.V 05.02.2021
Subjects:
Intelligent optimization algorithm
Multivariate calibration
Near-infrared spectroscopy
Particle swarm optimization
Variable selection
Intelligent optimization algorithm
Particle swarm optimization
Multivariate calibration
Variable selection
Near-infrared spectroscopy
ISSN:1386-1425, 1873-3557, 1873-3557
Online Access:Get full text
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Summary: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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ISSN:1386-1425
1873-3557
1873-3557
DOI:10.1016/j.saa.2020.118986