Hybrid methodology based on bayesian optimization and ga-parsimony for searching parsimony models by combining hyperparameter optimization and feature selection

Abstract

This paper presents a hybrid methodology that combines Bayesian Optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimonious models. The proposal is designed to reduce the computational efforts associated to the use of GA-PARSIMONY alone. The method is initialized with BO to obtain favorable initial model parameters. With these parameters, a constrained GA-PARSIMONY is implemented to generate accurate parsimonious models using feature reduction, data transformation and parsimonious model selection. Finally, a second BO is run again with the selected features. Experiments with Extreme Gradient Boosting Machines (XGBoost) and six UCI databases demonstrate that the hybrid methodology obtains analogous models than the GA-PARSIMONY but with a significant reduction on the execution time in five of the six datasets.