Biomedical classification application and parameters optimization of mixed kernel SVM based on the information entropy particle swarm optimization

Abstract The types of kernel function and relevant parameters’ selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (PSO): on the one hand, the generalization ability of classifier is effectively enhanced by constructing a mixed kernel function with global kernel function and local kernel function; on the other hand, the accuracy of classification is improved through optimization for related kernel parameters based on information entropy PSO. Compared with PSO-RBF kernel and PSO-mixed kernel, the improved PSO-mixed kernel SVM can effectively improve the classification accuracy through the classification experiment on biomedical datasets, which would not only prove the efficiency of this algorithm, but also show that the algorithm has good practical application value in biomedicine prediction.