Simultaneous feature selection and parameter optimisation of support vector machine using adaptive particle swarm gravitational search algorithm

Abstract

Particle swarm optimisation PSO and gravitational search algorithm GSA are two metaheuristics that have been used to solve both continuous and discrete problems. Furthermore, their hybridisation can enhance the algorithm performance in these two kinds of problems. However, their utilisation for mixed continuous-discrete problems has not been well investigated. One the other hand, feature selection and parameter optimisation are two important issues in machine learning. The aim of this work is to simultaneously explore both issues, proposing a mixed encoded population and variable representation of PSOGSA in order to select the relevant features and to optimise support vector machine parameters which has proved important predictive ability in feature selection. Furthermore, an adaptive mutation operator has been introduced into the hybrid PSOGSA algorithm. Experimental results on 10 benchmark data sets show that this proposed mixed approach can achieve high performance in both training and testing sets when comparing with PSO, GSA, PSOGSA and the genetic algorithm GA.