An ensemble method based on uninformative variable elimination and mutual information for spectral multivariate calibration.
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.
Tan
Chao
C
Department of Chemistry and Chemical engineering, Yibin University, Yibin, Sichuan 644007, PR China. chaotan1112@163.com
Wang
Jinyue
J
Wu
Tong
T
Qin
Xin
X
Li
Menglong
M
D000465:Algorithms; D002138:Calibration; D016018:Least-Squares Analysis; D008954:Models, Biological; D015999:Multivariate Analysis; D012044:Regression Analysis; D012984:Software; D013057:Spectrum Analysis / Q000379:methods* / Q000592:standards*