Training Support Vector Machines with an Heterogeneous Particle Swarm Optimizer

Abstract

Support vector machines are classification algorithms that have been successfully applied to problems in many different areas. Re- cently, evolutionary algorithms have been used to train support vector machines, which proved particularly useful in some multi-objective for- mulations and when indefinite kernels are used. In this paper, we propose a new heterogeneous particle swarm optimization algorithm, called scout- ing 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 classi- fication accuracy than the other evolutionary algorithms, while requiring significantly less computational resources.