Maximum-margin fuzzy classifier based on boundary

LIU Zhong-bao; WANG Shi-tong

doi:10.3788/OPE.20122001.0140

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Maximum-margin fuzzy classifier based on boundary

Article | 更新时间：2020-08-12

- Maximum-margin fuzzy classifier based on boundary
- Optics and Precision Engineering Vol. 20, Issue 1, Pages: 140-147(2012)
- 作者机构：
  
  1. 江南大学数字媒体学院,江苏无锡,214122
  2. 山西大学商务学院信息学院,山西太原,030031
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122001.0140
  CLC： TP391.4
- Received：19 July 2011，
  
  Revised：19 September 2011，
  
  Published Online：25 January 2012，
  
  Published：25 January 2012
- 稿件说明：
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刘忠宝, 王士同. 基于边界的最大间隔模糊分类器[J]. 光学精密工程, 2012,20(1): 140-147

LIU Zhong-bao, WANG Shi-tong. Maximum-margin fuzzy classifier based on boundary[J]. Editorial Office of Optics and Precision Engineering, 2012,20(1): 140-147
刘忠宝, 王士同. 基于边界的最大间隔模糊分类器[J]. 光学精密工程, 2012,20(1): 140-147 DOI： 10.3788/OPE.20122001.0140.

LIU Zhong-bao, WANG Shi-tong. Maximum-margin fuzzy classifier based on boundary[J]. Editorial Office of Optics and Precision Engineering, 2012,20(1): 140-147 DOI： 10.3788/OPE.20122001.0140.

摘要

对利用超平面、超(椭)球等几何形状实现数据分类的基于边界的主流分类方法进行了研究

在此基础上

提出了一种将空间点作为分类依据的最大间隔模糊分类器(MFC)。该方法首先在模式空间中找到一个模糊分类点

点距离两类样本要尽可能近且类间夹角间隔尽可能大。然后

测试样本通过

与训练样本间的最大化夹角间隔实现分类。最后

利用MFC的核化对偶式与最小包含球(MEB)的等价性

将MFC的应用范围从二类推广到单类。与主流分类方法的比较实验表明

MFC具有优良的分类性能和抗噪能力

其分类最高精度可达98.8%。

Abstract

Several kinds of current boundary classification methods based on hyperplane

hypersphere or ellipsoid were studied

and a novel classification method called Maximum-margin Fuzzy Classifier (MFC) was proposed by using a space point as the classification criterion. By the proposed method

a fuzzy classified point

was chosen in the pattern space firstly

which should be as close to two classes as possible. Moreover

the angle between the two classes should be also as large as possible. Then

the testing points could be classified in terms of the maximum angular margin between

and all the training points. Finally

the applications of the MFC were popularized from two-class classification to one-class classification according to the kernel dual of MFC equivalent to the Minimum Enclosed Ball (MEB). Comparative experiments with current classification methods verify that the MFC has good classification performance and noise resistance ability and its classification accuracy has been reached 98.9%.

关键词

Keywords

references

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