[1]黄华娟,韦修喜,周永权.基于模糊核聚类粒化的粒度支持向量机[J].智能系统学报,2019,14(06):1271-1277.[doi:10.11992/tis.201904048]
 HUANG Huajuan,WEI Xiuxi,ZHOU Yongquan.Granular support vector machine based on fuzzy kernel clustering granulation[J].CAAI Transactions on Intelligent Systems,2019,14(06):1271-1277.[doi:10.11992/tis.201904048]
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基于模糊核聚类粒化的粒度支持向量机(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1271-1277
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
Granular support vector machine based on fuzzy kernel clustering granulation
作者:
黄华娟1 韦修喜1 周永权12
1. 广西民族大学 信息科学与工程学院, 广西 南宁 530006;
2. 广西民族大学 广西高校复杂系统与智能计算重点实验室, 广西 南宁 530006
Author(s):
HUANG Huajuan1 WEI Xiuxi1 ZHOU Yongquan12
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
关键词:
模糊核聚类粒化支持向量机粒度支持向量机原空间核空间支持向量聚类
Keywords:
fuzzy kernel clustergranulationsupport vector machinegranular support vector machineoriginal spacekernel spacesupport vectorclustering
分类号:
TP18
DOI:
10.11992/tis.201904048
摘要:
针对传统的粒度支持向量机(granular support vector machine, GSVM)将训练样本在原空间粒化后再映射到核空间,导致数据与原空间的分布不一致,从而降低GSVM的泛化能力的问题,本文提出了一种基于模糊核聚类粒化的粒度支持向量机学习算法(fuzzy kernel cluster granular support vector machine, FKC-GSVM)。FKC-GSVM通过利用模糊核聚类直接在核空间对数据进行粒的划分和支持向量粒的选取,在相同的核空间中进行支持向量粒的GSVM训练。在UCI数据集和NDC大数据上的实验表明:与其他几个算法相比,FKC-GSVM在更短的时间内获得了精度更高的解。
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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备注/Memo

备注/Memo:
收稿日期:2019-04-18。
基金项目:国家自然科学基金资助项目(61662005);广西自然科学基金项目(2018JJA170121);广西高校中青年教师科研基础能力提升项目(2019KY0195).
作者简介:黄华娟,女,1984生,副教授,博士,主要研究方向为机器学习与数据挖掘。主持国家自然科学基金项目、广西自然科学基金项目各1项。发表学术论文20余篇;韦修喜,男,1980生,讲师,主要研究方向为人工智能。主持广西高校中青年教师科研基础能力提升项目1项。发表学术论文10余篇;周永权,男,1962年生,教授,博士,主要研究方向为计算智能。主持国家自然科学基金项目3项。发表学术论文100余篇。
通讯作者:韦修喜.E-mail:weixiuxi@163.com
更新日期/Last Update: 2019-12-25