Weighted Fusion of Multiple Models for Wavelength Selection
Abstract:A new method based on the weighted fusion of multiple models is presented for wavelength selection in multivariate calibration of spectral data. It fuses the regression coefficients of multiple models with weights based on minimum mean square error to improve the accuracy and stability of the wavelength selection. To validate the performance of the proposed method, it was applied to the partial least squares (PLS) modeling of three near-infrared spectral datasets and compared with full-spectrum PLS, genetic algorithm‐based PLS, and uninformative variable elimination‐based PLS methods. Results show that the proposed method can effectively select the informative wavelength and enhance the prediction ability of the PLS model. On account of its simpler algorithm and higher efficiency, it can be widely used in practical applications.
Document Type: Research Article
Affiliations: Key Laboratory of Fundamental Science on Micro/Nano-Device and System Technology, Chongqing University, Chongqing 400030, China
Publication date: July 1, 2013
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