Tuning parameter identification for variable selection algorithm using the sum of ranking differences algorithm

Abstract

AbstractVariable selection algorithms are often adopted to select the optimal variable from a full set of variables and are efficient for reducing the variable dimension and improving the model accuracy. Nonetheless, the parameters of the variable selection method and regression model, such as the number of latent variables of the partial least squares (PLS) model and the threshold value of the variable importance index, need to be identified. The parameters directly determine the final performance of the model. Currently, these parameters are often determined subjectively. As a result, the model results may be accidental because of the subjective determination of the parameters. To objectively identify these parameters, the sum of ranking differences (SRD) coupled with partial least squares‐variable importance in projection (PLS‐VIP‐SRD) and partial least squares‐uninformative variable elimination (PLS‐UVE‐SRD) algorithms was applied to determine the latent variable of the PLS model and the threshold value of the variable importance index. Furthermore, public near‐infrared data of corn were used as the calculation data. The final results show that the PLS‐VIP‐SRD and PLS‐UVE‐SRD models can more effectively and objectively determine the optimal parameter combination than the PLS‐VIP and PLS‐UVE models. Moreover, the selected variables are easier to interpret, and the prediction accuracy is also improved to some extent.