Abstract: Granular support vector machine will lead to loss of partial classification information and accuracy degradation while dividing granules and extracting representative points.To solve this problem,a learning strategy based on neighboring-boundary granular support vector machine (NGSVM) was proposed.Samples were divided into granules with kmeans method firstly,different granules were dealt with different rules to extract representative points,and then these representative points were put into fixed set or reduced set as requested,by which support vector machine (SVM) was trained.After completion of classifier,classification plane would be rectified by extracting neighboring-boundary samples according to the kernel distance.The simulation results show that NGSVM gains a higher classification accuracy by extracting neighboring-boundary samples near classification plane and fixing classification plane.

Key words: Neighboring-boundary,Granular support vector machine,Granules,Reduced set,Fixed set