Stacked Multivariate Calibration Analysis
10.3724/SP.J.1096.2010.00367
- VernacularTitle:叠加多元校正分析
- Author:
Wangdong NI
;
Ruilin MAN
- Publication Type:Journal Article
- Keywords:
Ensemble models(model fusion);
Multivariate calibration;
Stacked multivariate calibration;
Stacked moving-window multivariate calibration
- From:
Chinese Journal of Analytical Chemistry
2010;38(3):367-371
- CountryChina
- Language:Chinese
-
Abstract:
Two novel algorithms that employed the idea of ensemble models(stacked generalization or stacked regression), stacked multivariate calibration(PCR and PLS) and stacked moving-window multivariate calibration(PCR and PLS), were reported. The proposed algorithms established parallel, conventional PCR or PLS models based on all intervals of a set of spectra to take advantage of the information from the whole spectrum. Unlike traditional methods, they stack or incorporate these parallel models in a way to emphasize intervals(regions) highly related to the target property. These two stacking algorithms generate more parsimonious regression models with better predictive power than that of conventional PCR and PLS, and perform best when the spectral information is neither isolated to a single, small region, nor spread uniformly over the spectral data. The predictive performance of these two new algorithms is similar. Thus, two real NIR spectra were used here to show the improvement in predictive performance from these two new algorithms.
