Variable-weighted least-squares support vector machine for multivariate spectral analysis.
Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.
State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
Hong-Yan
HY
Zou
Hai-Long
HL
Wu
Hai-Yan
HY
Fu
Li-Juan
LJ
Tang
Lu
L
Xu
Jin-Fang
JF
Nie
Ru-Qin
RQ
Yu
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002138:Calibration; D005742:Gasoline / Q000032:analysis; D016018:Least-Squares Analysis; D008460:Meat / Q000032:analysis; D015999:Multivariate Analysis; D013057:Spectrum Analysis / Q000379:methods*
D005742:Gasoline