Title: A novel granular support vector machine based on fuzzy kernel clustering

Authors: Huajuan Huang; Shifei Ding

Addresses: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing, 100190, China; Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, China ' School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China; Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Science, Beijing, 100190, China; Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

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.

Keywords: fuzzy kernel clustering; FKC; granulation; granular SVM; support vector machines; GSVM; fuzzy clustering.

DOI: 10.1504/IJCI.2015.071210

International Journal of Collaborative Intelligence, 2015 Vol.1 No.2, pp.153 - 166

Received: 04 Jun 2013
Accepted: 05 Jun 2013
Published online: 17 Aug 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article