[1]史颂辉,丁世飞.基于能量的结构化最小二乘孪生支持向量机[J].智能系统学报,2020,15(5):1013-1019.[doi:10.11992/tis.201906030]
 SHI Songhui,DING Shifei.Energy-based structural least square twin support vector machine[J].CAAI Transactions on Intelligent Systems,2020,15(5):1013-1019.[doi:10.11992/tis.201906030]
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基于能量的结构化最小二乘孪生支持向量机(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年5期
页码:
1013-1019
栏目:
吴文俊人工智能科学技术奖论坛
出版日期:
2020-09-05

文章信息/Info

Title:
Energy-based structural least square twin support vector machine
作者:
史颂辉12 丁世飞12
1. 中国矿业大学 计算机科学与技术学院,江苏 徐州 221116;
2. 矿山数字化教育部工程研究中心,江苏 徐州 221116
Author(s):
SHI Songhui12 DING Shifei12
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China;
2. Engineering Research Center (Ministry of Education) of Mine Digitization, Xuzhou 221116, China
关键词:
孪生支持向量机最小二乘结构信息聚类协方差矩阵能量因子“多对一”策略多分类问题
Keywords:
twin support vector machineleast squarestructural informationclustercovariance matrixenergy factor“all-versus-one” strategymulti-class classification problem
分类号:
TP391
DOI:
10.11992/tis.201906030
文献标志码:
A
摘要:
针对最小二乘孪生支持向量机对噪声和离群值非常敏感的问题,本文提出了一种基于能量的结构化最小二乘孪生支持向量机。首先对每个类进行聚类分析,然后计算类中各个簇的协方差矩阵并将其引入到目标函数中。其次,为了降低噪声和离群值对算法的影响,本文为每个超平面引入能量因子,在最小二乘的基础上将等式约束转换为基于能量的形式。最后采用“多对一”的策略将提出的算法用于处理多分类问题。研究结果表明:本文提出的基于能量的结构化最小二乘孪生支持向量机具有良好的分类性能。
Abstract:
The least square twin support vector machine (TWSVM) is very sensitive to noise and outlier. To solve this problem, we propose an energy-based structured least square TWSVM (ES-LSTWSVM). First, we perform a cluster analysis on each class, then compute the covariance matrix of each cluster in the classes, and introduce it into the objective function. To reduce the influence of noises and outliers on the algorithm, an energy factor is introduced to each hyperplane, and the equality constraint is converted into an energy-based form on the basis of least squares. Finally, we adopt the “all-versus-one” strategy to apply the proposed algorithm in solving a multi-class classification problem. The experimental results show that ES-LSTWSVM has good classification performance.

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相似文献/References:

[1]李景灿,丁世飞.基于人工鱼群算法的孪生支持向量机[J].智能系统学报,2019,14(6):1121.[doi:10.11992/tis.201905025]
 LI Jingcan,DING Shifei.Twin support vector machine based on artificial fish swarm algorithm[J].CAAI Transactions on Intelligent Systems,2019,14(5):1121.[doi:10.11992/tis.201905025]

备注/Memo

备注/Memo:
收稿日期:2019-06-18。
基金项目:国家自然科学基金项目(61672522,61976216,61379101)
作者简介:史颂辉,硕士研究生,主要研究方向为支持向量机、机器学习;丁世飞,教授,博士生导师,主要研究方向为人工智能、机器学习、模式识别、数据挖掘。主持国家重点基础研究计划项目1项、国家自然科学基金面上项目3项。发表学术论文200余篇,出版专著5部
通讯作者:丁世飞.E-mail:dingsf@cumt.edu.cn
更新日期/Last Update: 2021-01-15