A GSA-SVM HYBRID SYSTEM FOR CLASSIFICATION OF BINARY PROBLEMS
Soroor Sarafrazi, Hossein Nezamabadi–pour, Mojgan Barahman, Nader Nassif Barsoum, Jeffrey Frank Webb, Pandian Vasant · AIP conference proceedings · 2011
This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA‐SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)‐SVM and genetic algorithm (GA)‐SVM, which are two hybrid systems for classification.