[1]花小朋,孙一颗,丁世飞.一种改进的投影孪生支持向量机[J].智能系统学报编辑部,2016,11(3):384-389.[doi:10.11992/tis.201603049]
　HUA Xiaopeng,SUN Yike,DING Shifei.An improved projection twin support vector machine[J].CAAI Transactions on Intelligent Systems,2016,11(3):384-389.[doi:10.11992/tis.201603049]
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年3期
页码:
384-389
栏目:
出版日期:
2016-06-25

文章信息/Info

Title:
An improved projection twin support vector machine
作者:
花小朋1;  孙一颗1;  丁世飞2
1. 盐城工学院 信息工程学院, 江苏 盐城 224051;
2. 中国矿业大学 计算机学院, 江苏 徐州 221116
Author(s):
HUA Xiaopeng1;  SUN Yike1;  DING Shifei2
1. School of Information Engineering, Yancheng Institute of Technology, Yancheng 224051, China;
2. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
关键词:
分类; 投影孪生支持向量机; 局部信息; 加权均值; 近邻图; 二次规划; 约束条件; 时间复杂度
Keywords:
classification; projection twin support vector machine; local information; weighted mean; neighborhood graph; quadratic programming; constraint condition; time complexity
分类号:
TP391.4
DOI:
10.11992/tis.201603049
摘要:
针对投影孪生支持向量机(PTSVM)在训练阶段欠考虑样本空间局部结构和局部信息的缺陷,提出一种具有一定局部学习能力的有监督分类方法:加权投影孪生支持向量机(weighted PTSVM,WPTSVM)。相比于PTSVM,WPTSVM优势在于:通过构造类内近邻图为每个样本获取特定的权值,并且以加权均值取代标准均值,在一定程度上提高了算法的局部学习能力;选取异类样本集中少量边界点构造优化问题的约束条件,很大程度上降低了二次规划求解的时间复杂度;继承了PTSVM的优点,可以看成PTSVM的推广算法。理论分析及其在人造数据集和真实数据集上的测试结果表明该方法具有上述优势。
Abstract:
A supervised classification method having a local learning ability, called weighted projection twin support vector machine (WPTSVM), is proposed. This method aims to improve upon a defect that projection twin support vector machines (PTSVMs) have, namely, that PTSVMs do not take account of the local structure and local information of a sample space in the training process. Compared with PTSVM, WPTSVM improves its local learning ability to some extent by attaching different weights for each sample according to the within-class neighborhood graph and replacing the standard mean with a weighted mean. Moreover, to reduce computational complexity, WPTSVM chooses a small number of boundary points in the contrary-class based on the between-class neighborhood graph to construct constraints of the original optimization problems. The method inherits the merits of PTSVM and can be regarded as an improved version of PTSVM. Experimental results on artificial and real datasets indicate the effectiveness of the WPTSVM method.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-3-20;改回日期:。
基金项目:国家重点基础研究计划项目(2013CB329502);国家自然科学基金项目(61379101);江苏省自然科学基金项目(BK20151299).
作者简介:花小朋,男,1975年生,副教授,博士,主要研究方向为机器学习与数据挖掘,发表学术论文10余篇。孙一颗,男,1993年生,硕士研究生,主要研究方向为机器学习、数据挖掘,申请发明专利2项。丁世飞,男,1963年生,教授,博士生导师,中国计算机学会高级会员,中国人工智能学会高级会员,江苏省计算机学会人工智能专业委员会委员,主要研究方向为智能信息处理,目前主持国家973项目1项、国家自然科学基金项目1项,发表学术论文60余篇。
通讯作者:花小朋.E-mail:xp_hua@163.com.
更新日期/Last Update: 1900-01-01