河北大学学报(自然科学版) ›› 2017, Vol. 37 ›› Issue (2): 187-193.DOI: 10.3969/j.issn.1000-1565.2017.02.013

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引入结构信息的模糊支持向量机

李凯1,顾丽凤1,张雷2   

  • 收稿日期:2016-08-04 出版日期:2017-03-25 发布日期:2017-03-25
  • 作者简介:李凯(1963—),男,河北满城人,河北大学教授,博士,主要从事机器学习、数据挖掘和模式识别研究. E-mail:likai@hbu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61375075)

Fuzzy support vector machine based on structural information

LI Kai1, GU Lifeng1,ZHANG Lei2   

  1. 1.College of Computer Science and Technology, Hebei University, Baoding 071002, China; 2.Department of Network and Information Security, Baoding City Branch of China UnitedNetwork Communication Corporation Limited, Baoding 071051, China
  • Received:2016-08-04 Online:2017-03-25 Published:2017-03-25

摘要: 模糊支持向量机是在支持向量机的基础上,通过考虑不同样本对支持向量机的作用而提出的一种分类方法,然而,该方法却忽视了给定样本集的结构信息.为此,将样本集中的结构信息引入到模糊支持向量机中,给出了一种结构型模糊支持向量机模型,利用拉格朗日求解方法,将其转换为一个具有约束条件的优化问题,通过求解该对偶问题,获得了结构型模糊支持向量机分类器.实验中选取标准数据集,验证了提出方法的有效性.

关键词: 支持向量机, 模糊支持向量机, 结构信息, 类内离散度

Abstract: By considering the role of different samples on support vector machine, fuzzy support vector machine is presented based on support vector machine. However, it ignores the structural information of the given sample set. To this end, structural information of the given sample set is introduced into fuzzy support vector machine and obtained a structural fuzzy support vector machine model. It is converted to dual problem with quadratic programming using Lagrange method. Through solving this dual problem,the fuzzy support vector machine classifier is obtained. Experimental results in selected standard data sets demonstrate the effectiveness of the proposed method.

Key words: support vector machines, fuzzy support vector machine, structure information

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