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An efficient variable selection method based on random frog for the multivariate calibration of NIR spectra

Variable selection is a critical step for spectrum modeling. In this study, a new method of variable interval selection based on random frog (RF), known as Interval Selection based on Random Frog (ISRF), is developed. In the ISRF algorithm, RF is used to search the most likely informative variables...

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Detalles Bibliográficos
Autores principales: Sun, Jingjing, Yang, Wude, Feng, Meichen, Liu, Qifang, Kubar, Muhammad Saleem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9052783/
https://www.ncbi.nlm.nih.gov/pubmed/35498850
http://dx.doi.org/10.1039/d0ra00922a
Descripción
Sumario:Variable selection is a critical step for spectrum modeling. In this study, a new method of variable interval selection based on random frog (RF), known as Interval Selection based on Random Frog (ISRF), is developed. In the ISRF algorithm, RF is used to search the most likely informative variables and then, a local search is applied to expand the interval width of the informative variables. Through multiple runs and visualization of the results, the best informative interval variables are obtained. This method was tested on three near infrared (NIR) datasets. Four variable selection methods, namely, genetic algorithm PLS (GA-PLS), random frog, interval random frog (iRF) and interval variable iterative space shrinkage approach (iVISSA) were used for comparison. The results show that the proposed method is very efficient to find the best interval variables and improve the model's prediction performance and interpretation.