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.

Keywords:

• Mixed kernel function
• information entropy
• particle swarm algorithm
• SVM
• kernel function

Disclosure statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.

Funding

This work is supported by the National Basic Research Program of China (2014CB744600), the National Natural Science Foundation of China (61602017 and 61420106005), the Beijing Outstanding Talent Training Foundation (2014000020124G039), the Beijing Natural Science Foundation (4164080), the International Science & Technology Cooperation Program of China (2013DFA32180), the Grant-in-Aid for Scientific Research (C) from Japan Society for the Promotion of Science (26350994), the Beijing Municipal Science and Technology Project (D12100005012003), the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding (ZY201403), and the Beijing Municipal Science and technology achievement transformation and industrialization projects funds (Z121100006112057).