VCPA-based hybrid strategy

A hybrid variable selection strategy based on continuous shrinkage of variable space https://doi.org/10.1016/j.aca.2019.01.022

A hybrid variable selection strategy based on continuous shrinkage of variable space in multivariate calibration
https://doi.org/10.1016/j.aca.2019.01.022

An overview of variable selection methods in multivariate analysis of near-infrared spectra
https://doi.org/10.1016/j.trac.2019.01.018

In this study, we propose a hybrid variable selection strategy based on the continuous shrinkage of variable space which is the core idea of variable combination population analysis (VCPA). The VCPA-based hybrid strategy continuously shrinks the variable space from big to small and optimizes it based on modified VCPA in the first step. It then employs iteratively retaining informative variables (IRIV) and a genetic algorithm (GA) to carry out further optimization in the second step. It takes full advantage of VCPA, GA, and IRIV, and makes up for their drawbacks in the face of high numbers of variables. Three NIR datasets and three variable selection methods including two widely-used methods (competitive adaptive reweighted sampling, CARS and genetic algorithm-interval partial least squares, GA–iPLS) and one hybrid method (variable importance in projection coupled with genetic algorithm, VIP–GA) were used to investigate the improvement of VCPA-based hybrid strategy.