LAUSR

Abstract This article presents a hybrid methodology that combines Bayesian optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimony models. The proposal is designed to reduce the sizeable computational effort associated with the use of GA-PARSIMONY alone. The method begins with BO to obtain favorable initial model parameters. Then, with these parameters, a constrained GA-PARSIMONY is implemented to generate accurate parsimony models by using feature reduction, data transformation and parsimonious model selection. Experiments with extreme gradient boosting machines (XGBoost) and ten UCI databases demonstrated that the hybrid methodology obtains models analogous to those of GA-PARSIMONY while achieving significant reductions in elapsed time in eight out of ten datasets.