A hybrid switching PSO algorithm and support vector machines for bankruptcy prediction

In this paper, a hybrid switching particle swarm optimization (SPSO) and support vector machine (SVM) algorithm is proposed for jointly applying to the problem of bankruptcy prediction. The main purpose of this paper is to handle better explanatory power and stability of the SVM. More specifically, a recently developed Switching PSO algorithm is used to find out the optimal parameter values of radial basis function (RBF) kernel of SVM. The sample data sets come from UCI Machine Learning Repository donated on 9^th Feb. 2014. It is shown that the proposed algorithm gives much improved performance over the traditional SVM-based methods combined with genetic algorithm (GA) or particle swarm optimization (PSO).