计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (16): 142-144.

• 数据库、信号与信息处理 • 上一篇    下一篇

非均衡数据的去噪模糊支持向量机新方法

张桂香1,费 岚1,杜 喆2,刘三阳2   

  1. 1.河南财经学院 电教计算中心,郑州 450003
    2.西安电子科技大学 数学科学系,西安 710071
  • 收稿日期:2007-09-12 修回日期:2007-11-08 出版日期:2008-06-01 发布日期:2008-06-01
  • 通讯作者: 张桂香

New noise-immune fuzzy SVM algorithm for unbalanced data

ZHANG Gui-xiang1,FEI Lan1,DU Zhe2,LIU San-yang2   

  1. 1.Computing Centre of Electrifying Education,Henan University of Finance and Economics,Zhengzhou 450003,China
    2.The Department of Applied Mathematics,Xidian University,Xi’an 710071,China
  • Received:2007-09-12 Revised:2007-11-08 Online:2008-06-01 Published:2008-06-01
  • Contact: ZHANG Gui-xiang

摘要: 针对支持向量机对噪声的敏感,以及当两类训练样本数量差别悬殊时,造成分类结果倾向较大类等弱点,通过理论分析,合理地设计隶属度函数,提出了一种新隶属度函数的模糊支持向量机。该方法既可补偿倾向性造成的不利影响,又可增加抗噪声能力,提高预测分类精度。最后通过对含噪声的非均衡数据实验表明,该方法比传统支持向量机和简单去噪模糊支持向量机都有着较高的分类能力。

关键词: 支持向量机, 非均衡数据, 分类, 隶属度函数

Abstract: Since SVM is sensitive to noises or outliers in the training set and the classification of unbalance data is unfair to the rare class,a new fuzzy Support Vector Machine is presented with theoretical analysis given.By properly designing a new fuzzy membership function,the proposed algorithm can compensate the ill-effect of tendency and also can strengthen the ability to detect noises thus improves the accuracy.Simulations on unbalenced data with noise show that,compared with traditional SVM and FSVM,this algorithm has better classification ability.

Key words: Support Vector Machine, unbalanced data, classification, membership function