Title: Simultaneous feature selection and parameter optimisation of support vector machine using adaptive particle swarm gravitational search algorithm
Authors: Malek Sarhani; Abdellatif El Afia
Addresses: ENSIAS, Mohammed V University, Madinat Irfane, Rabat, Morocco ' ENSIAS, Mohammed V University, Madinat Irfane, Rabat, Morocco
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).
Keywords: adaptive mutation operator; feature selection; parameter optimisation; gravitational search algorithm; GSA; particle swarm optimisation; PSO; support vector machines; SVM.
International Journal of Metaheuristics, 2016 Vol.5 No.1, pp.51 - 66
Received: 11 Nov 2015
Accepted: 15 Jul 2016
Published online: 12 Sep 2016 *