Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization

In this paper, we propose a Fireworks Algorithm based method to improve the performance of feature selection and parameters optimization in training SVM. For the 0-1 characteristic of feature selection, the binary coding Fireworks Algorithm and RBF kernel function based SVM are used to improve the accuracy of classification with less features. Compared to previous works, the proposed method can avoid being mature and falling into a local value, and it can effectively find the appropriate feature subset and parameters to get better performance of classification in UCI dataset.