Support Vector Machine Optimization Based On Magnetic Bacteria Optimization Algorithm

Classification performance of support vector machine (SVM) will be influenced by its model parameters. For this problem, a new method named magnetic bacteria optimization algorithm (MBOA) that optimizes the parameters of SVM is proposed. It is tested by the UCI standard data sets and compared with the other optimization algorithms, such as particle swarm optimization (PSO). Experimental results show that the MBOA can optimize the parameters of SVM well and has better performance than the compared algorithms.