Training Support Vector Machines with an Heterogeneous Particle Swarm Optimizer

摘要

Support vector machines are classification algorithms that have been successfully applied to problems in many different areas. Recently, evolutionary algorithms have been used to train support vector machines, which proved particularly useful in some multi-objective formulations and when indefinite kernels are used. In this paper, we propose a new heterogeneous particle swarm optimization algorithm, called scouting predator-prey optimizer, specially adapted for the training of support vector machines. We compare our algorithm with two other evolutionary approaches, using both positive definite and indefinite kernels, on a large set of benchmark problems. The experimental results confirm that the evolutionary algorithms can be competitive with the classic methods and even superior when using indefinite kernels. The scouting predator-prey optimizer can train support vector machines with similar or better classification accuracy than the other evolutionary algorithms, while requiring significantly less computational resources.