Efficient variable selection algorithm adopting variance inflated resampling weight vector into model population analysis

Abstract

AbstractVariable iterative space shrinkage approach (VISSA) is an important variable selection algorithm known for its improved accuracy and outcome stability in partial least squares (PLS) regression models. However, time efficiency of VISSA is not very promising. In this work, three strategies to inflate the variance of resampling weight vector (RWV) have been proposed to accelerate the space shrinkage in VISSA. The original RWV (ie, average binary frequency of variables) is replaced with average of unit normalized regression coefficients (UNRC), fitness normalized regression coefficients (FNRC), or logarithmically transformed regression coefficients (LTRC) of selected PLS sub‐models. Although prediction efficiencies for UNRC and FNRC are marginally inferior to the original binary‐weight VISSA, the stability of retained variables and remarkable improvements in algorithm speed is evident for relatively large size NIR data set (700 variables). LTRC with moderate degree of RWV variance inflation is indisputably a better choice. Chimeric algorithm, incorporating UNRC, FNRC, or LTRC in first round of original VISSA, maintained the model fitness per se while significantly improving time efficiency. With small dimensional NIR data set (100 variables), proposed weighting schemes have no additional advantage over original VISSA implementation.