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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Bibliographic Details
Published in:Molecules (Basel, Switzerland) Vol. 24; no. 3; p. 421
Main Authors: Chen, Yuanyuan, Wang, Zhibin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 24.01.2019
MDPI
Subjects:
binary dragonfly algorithm
ensemble learning
Exploitation
Feature selection
Genetic algorithms
Methods
NIR spectroscopy
Optimization
Population
Quantitative analysis
quantitative analysis modeling
Random variables
Sparsity
Spectrum analysis
wavelength selection
ensemble learning
quantitative analysis modeling
wavelength selection
binary dragonfly algorithm
NIR spectroscopy
ISSN:1420-3049, 1420-3049
Online Access:Get full text
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Summary: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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ISSN:1420-3049
1420-3049
DOI:10.3390/molecules24030421