A novel granular support vector machine based on fuzzy kernel clustering

Abstract

For the traditional granular support vector machine (GSVM), the training samples are granulated in the original space and then are mapped into the kernel space. However, this method will lead to the inconsistent distribution of the data between original space and kernel space, thereby reducing the generalisation of GSVM. To solve this problem, a method called granular support vector machine based on fuzzy kernel cluster is proposed. This method directly granulates the training data and selects support vector particles in kernel space, and then trains the support vector particles in the same kernel space by GSVM. Finally, the experiments on benchmark datasets demonstrate the effectiveness of the proposed approach.