An ensemble method based on uninformative variable elimination and mutual information for spectral multivariate calibration.

Autor: Tan C; Department of Chemistry and Chemical engineering, Yibin University, Yibin, Sichuan 644007, PR China. chaotan1112@163.com, Wang J, Wu T, Qin X, Li M
Jazyk: angličtina
Zdroj: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy [Spectrochim Acta A Mol Biomol Spectrosc] 2010 Dec; Vol. 77 (5), pp. 960-4. Date of Electronic Publication: 2010 Aug 27.
DOI: 10.1016/j.saa.2010.08.031
Abstrakt: Based on the combination of uninformative variable elimination (UVE), bootstrap and mutual information (MI), a simple ensemble algorithm, named ESPLS, is proposed for spectral multivariate calibration (MVC). In ESPLS, those uninformative variables are first removed; and then a preparatory training set is produced by bootstrap, on which a MI spectrum of retained variables is calculated. The variables that exhibit higher MI than a defined threshold form a subspace on which a candidate partial least-squares (PLS) model is constructed. This process is repeated. After a number of candidate models are obtained, a small part of models is picked out to construct an ensemble model by simple/weighted average. Four near/mid-infrared (NIR/MIR) spectral datasets concerning the determination of six components are used to verify the proposed ESPLS. The results indicate that ESPLS is superior to UVEPLS and its combination with MI-based variable selection (SPLS) in terms of both the accuracy and robustness. Besides, from the perspective of end-users, ESPLS does not increase the complexity of a calibration when enhancing its performance.
(Copyright © 2010 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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