Granular support vector machine based on mixed measure

Abstract

This paper presents a granular support vector machine learning model based on mixed measure, namely M_GSVM, to solve the model error problem produced by mapping, simplifying, granulating or substituting of data for traditional granular support vector machines (GSVM). For M_GSVM, the original data will be mapped into the high-dimensional space by mercer kernel. Then, the data are divided into some granules, and those mixed granules including more information are extracted and trained by support vector machine (SVM). Finally, the decision hyperplane will be corrected through geometric analyzing to reduced model error effectively. The experiment results on UCI benchmark datasets and Interacting Proteins database demonstrate that the proposed M_GSVM model can improve the generalization performance greatly with high learning efficiency synchronously.