Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization

Recently, applications of Internet of Things create enormous volumes of data, which are available for classification and prediction. Classification of big data needs an effective and efficient metaheuristic search algorithm to find the optimal feature subset. Cat swarm optimization (CSO) is a novel metaheuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two submodes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection and parameter optimization of support vector machines (SVMs). Experiment results demonstrate the superiority of MCSO in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original CSO algorithm. Moreover, experiment results show the fittest CSO parameters and MCSO take less training time to obtain results of higher accuracy than original CSO. Therefore, MCSO is suitable for real-world applications.