An intelligent model selection scheme based on particle swarm optimization

Abstract

To improve the learning efficiency of support vector machine, an intelligent model selection scheme based on particle swarm optimization (PSO) was presented to optimize the hyper-parameters. By taking the model selection problem as a multi-object optimization problem, one can obtain a solution set known as Pareto front; each one model in this set is non-dominated. PSO was used to solve the above muti-objective optimization problem and then the model set was obtained. The scheme was tested on several datasets, the results show that Pareto front can be obtained in one trial and the effect of every single parameter can be displayed more directly.