Variable space boosting partial least squares for multivariate calibration of near-infrared spectroscopy
作者：Xihui Bian*, Shujuan Li, Xueguang Shao, Peng Liu
關(guān)鍵字：Boosting, Near-infrared, Partial least squares, Variable space, Ensemble modeling
論文來源：期刊
具體來源：Chemometrics and Intelligent Laboratory Systems, 2016, 158, 174-179
發(fā)表時(shí)間：2016年
A novel boosting strategy
by establishing sub-model from variable direction named variable space boosting
partial least squares (VS-BPLS) was proposed for multivariate calibration of
near-infrared (NIR) spectroscopy. At the
first cycle, all the variables in the training set are given the same
sampling weights and then a certain number of variables are selected to build
PLS sub-model according to the distribution of the sampling
weights. In the following cycles, the sampling weights of the variables in the training set are given by a
predefined loss function. This loss function is about the error of known and
predicted spectra that is obtained by the product of score and loading of PLS
sub-models. The final prediction for
unknown sample is obtained by the weighted average of each prediction of
all the sub-models. The proposed method not only can solve the small sample
problem, but also remove redundant information in variables. The performance of
VS-BPLS is tested with two NIR spectral datasets. As comparisons to VS-BPLS,
the conventional PLS and two variable selection methodMonte
Carlouninformative variable elimination PLS (MCUVE-PLS) and randomization
test PLS (RT-PLS) have also been investigated. Results show that VS-BPLS has a
superiority for small sample problems in prediction accuracy and stability compared
with the PLS, MCUVE-PLS and RT-PLS.