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Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm

Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, the...

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Autores principales: Chen, Yuanyuan, Wang, Zhibin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6384923/
https://www.ncbi.nlm.nih.gov/pubmed/30682788
http://dx.doi.org/10.3390/molecules24030421
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author Chen, Yuanyuan
Wang, Zhibin
author_facet Chen, Yuanyuan
Wang, Zhibin
author_sort Chen, Yuanyuan
collection PubMed
description Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve; (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results.
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spelling pubmed-63849232019-02-23 Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm Chen, Yuanyuan Wang, Zhibin Molecules Article Wavelength selection is an important preprocessing issue in near-infrared (NIR) spectroscopy analysis and modeling. Swarm optimization algorithms (such as genetic algorithm, bat algorithm, etc.) have been successfully applied to select the most effective wavelengths in previous studies. However, these algorithms suffer from the problem of unrobustness, which means that the selected wavelengths of each optimization are different. To solve this problem, this paper proposes a novel wavelength selection method based on the binary dragonfly algorithm (BDA), which includes three typical frameworks: single-BDA, multi-BDA, ensemble learning-based BDA settings. The experimental results for the public gasoline NIR spectroscopy dataset showed that: (1) By using the multi-BDA and ensemble learning-based BDA methods, the stability of wavelength selection can improve; (2) With respect to the generalized performance of the quantitative analysis model, the model established with the wavelengths selected by using the multi-BDA and the ensemble learning-based BDA methods outperformed the single-BDA method. The results also indicated that the proposed method is not limited to the dragonfly algorithm but can also be combined with other swarm optimization algorithms. In addition, the ensemble learning idea can be applied to other feature selection areas to obtain more robust results. MDPI 2019-01-24 /pmc/articles/PMC6384923/ /pubmed/30682788 http://dx.doi.org/10.3390/molecules24030421 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Yuanyuan
Wang, Zhibin
Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm
title Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm
title_full Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm
title_fullStr Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm
title_full_unstemmed Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm
title_short Wavelength Selection for NIR Spectroscopy Based on the Binary Dragonfly Algorithm
title_sort wavelength selection for nir spectroscopy based on the binary dragonfly algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6384923/
https://www.ncbi.nlm.nih.gov/pubmed/30682788
http://dx.doi.org/10.3390/molecules24030421
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