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A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling
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DOI：10.1016/j.saa.2020.118986.png)
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Abstract
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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. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Variable selection
Multivariate calibration
Intelligent optimization algorithm
Particle swarm optimization
Near-infrared spectroscopy
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