Fast optimization of hyperparameters for support vector regression models with highly predictive ability

• Three hyperparameters must be optimized beforehand in SVR modeling.
• Grid search (GS) and cross-validation (CV) take enormous time for the optimization.
• Theoretical decision cannot consider predictive ability of SVR models.
• We proposed a new optimization method based on theoretical techniques, and GS and CV.
• The proposed method could appropriately optimize SVR hyperparameters with little time.

Support vector regression (SVR) attracts much attention in chemometrics as a nonlinear regression method due to its theoretical background. In SVR modeling, three hyperparameters must be set beforehand. The optimization method based on grid search (GS) and cross-validation (CV) is employed normally in the selection of the SVR hyperparameters. However, this takes enormous time. Although theoretical techniques exist to decide the values of the SVR hyperparameters, predictive ability of SVR models is not considered in the decision. We therefore proposed a method based on the GS and CV method and theoretical techniques for fast optimization of the SVR hyperparameters, considering predictive ability of SVR models. After values of two hyperparameters are decided theoretically, each hyperparameter is optimized independently with GS and CV. The highly predictive ability of SVR models and small computational time for the proposed method are confirmed through the case studies using real data sets.