Support vector machine parameter tuning based on particle swarm optimization metaheuristic

This paper introduces a method for linear support vector machine parameter tuning based on particle swarm optimization metaheuristic, which is used to find the best cost (penalty) parameter for a linear support vector machine to increase textual data classification accuracy. Additionally, majority voting based ensembling is applied to increase the efficiency of the proposed method. The results were compared with results from our previous research and other authors' works. They indicate that the proposed method can improve classification performance for a sentiment recognition task.