[1]HUANG Huajuan,WEI Xiuxi,ZHOU Yongquan.Granular support vector machine based on fuzzy kernel clustering granulation[J].CAAI Transactions on Intelligent Systems,2019,14(6):1271-1277.[doi:10.11992/tis.201904048]
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Granular support vector machine based on fuzzy kernel clustering granulation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1271-1277 Column: 学术论文—机器学习 Public date: 2019-11-05
Title:
Granular support vector machine based on fuzzy kernel clustering granulation
Author(s):
HUANG Huajuan1;  WEI Xiuxi1;  ZHOU Yongquan1; 2
1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China;
2. Guangxi Higher School Key Laboratory of Complex Systems and Intelligent Computing, Guangxi University for Nationalities, Nanning 530006, China
Keywords:
fuzzy kernel cluster; granulation; support vector machine; granular support vector machine; original space; kernel space; support vector; clustering
CLC:
TP18
DOI:
10.11992/tis.201904048
Abstract:
For the traditional granular support vector machine (GSVM), the training samples are granulated in the original space and then mapped to the kernel space. However, this method will lead to the inconsistent distribution of the data between the original space and the kernel space, thereby reducing the generalization of GSVM. To solve this problem, a granular support vector machine based on fuzzy kernel cluster is proposed. Here, the training data are directly granulated, and support vector particles are selected in kernel space. The support vector particles are then trained in the same kernel space by the GSVM. Finally, experiments on UCI data sets and NDC big data sets show that FKC-GSVM achieves more accurate solutions in a shorter time than other algorithms.
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