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[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

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 15 期数: 2020年第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

Title:: Energy-based structural least square twin support vector machine

作者:: 史颂辉^1,2, 丁世飞^1,2; 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 Songhui^1,2, DING Shifei^1,2; 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 machine; least square; structural information; cluster; covariance matrix; energy factor; “all-versus-one” strategy; multi-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.

参考文献/References:: [1] CORTES C, VAPNIK V N. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[2] JAYADEVA, KHEMCHANDANI R, CHANDRA S. Twin support vector machines for pattern classification[J]. IEEE transactions on pattern analysis and machine intelligence, 2007, 29(5): 905-910.
[3] SHAO Yuanhai, ZHANG Chunhua, WANG Xiaobo, et al. Improvements on twin support vector machines[J]. IEEE transactions on neural networks, 2011, 22(6): 962-968.
[4] KUMAR M A, GOPAL M. Least squares twin support vector machines for pattern classification[J]. Expert systems with applications, 2009, 36(4): 7535-7543.
[5] TIAN Yingjie, QI Zhiquan, JU Xuchan, et al. Nonparallel support vector machines for pattern classification[J]. IEEE transactions on cybernetics, 2014, 44(7): 1067-1079.
[6] NASIRI J A, CHARKARI N M, MOZAFARI K. Energy-based model of least squares twin support vector machines for human action recognition[J]. Signal processing, 2014, 104: 248-257.
[7] 马跃峰, 梁循, 周小平. 一种基于全局代表点的快速最小二乘支持向量机稀疏化算法[J]. 自动化学报, 2017, 43(1): 132-141
MA Yuefeng, LIANG Xun, ZHOU Xiaoping. A fast sparse algorithm for least squares support vector machine based on global representative points[J]. Acta automatica sinica, 2017, 43(1): 132-141
[8] 吴青, 齐韶维, 孙凯悦, 等. 最小二乘大间隔孪生支持向量机[J]. 北京邮电大学学报, 2018, 41(6): 34-38
WU Qing, QI Shaowei, SUN Kaiyue, et al. Least squares large margin twin support vector machine[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(6): 34-38
[9] RIGOLLET P. Generalization error bounds in semi-supervised classification under the cluster assumption[J]. The journal of machine learning research, 2007, 8: 1369-1392.
[10] YEUNG D S, WANG Defeng, NG W W Y, et al. Structured large margin machines: sensitive to data distributions[J]. Machine learning, 2007, 68(2): 171-200.
[11] LANCKRIET G R G, EL GHAOUI L, BHATTACHARYYA C, et al. A robust minimax approach to classification[J]. The journal of machine learning research, 2002, 3: 555-582.
[12] HUANG Kaizhu, YANG Haiqin, KING I, et al. Maxi-min margin machine: learning large margin classifiers locally and globally[J]. IEEE transactions on neural networks, 2008, 19(2): 260-272.
[13] XUE Hui, CHEN Songcan, YANG Qiang. Structural regularized support vector machine: a framework for structural large margin classifier[J]. IEEE transactions on neural networks, 2011, 22(4): 573-587.
[14] QI Zhiquan, TIAN Yingjie, SHI Yong. Structural twin support vector machine for classification[J]. Knowledge-based systems, 2013, 43: 74-81.
[15] 丁世飞, 张健, 张谢锴, 等. 多分类孪生支持向量机研究进展[J]. 软件学报, 2018, 29(1): 89-108
DING Shifei, ZHANG Jian, ZHANG Xiekai, et al. Survey on multi class twin support vector machines[J]. Journal of software, 2018, 29(1): 89-108
[16] WARD JR J H. Hierarchical grouping to optimize an objective function[J]. Journal of the American statistical association, 1963, 58(301): 236-244.
[17] 陶莹, 杨锋, 刘洋, 等. K均值聚类算法的研究与优化[J]. 计算机技术与发展, 2018, 28(6): 90-92
TAO Ying, YANG Feng, LIU Yang, et al. Research and optimization of K-means clustering algorithm[J]. Computer technology and development, 2018, 28(6): 90-92
[18] XU Xiao, DING Shifei, DU Mingjing, et al. DPCG: an efficient density peaks clustering algorithm based on grid[J]. International journal of machine learning and cybernetics, 2018, 9(5): 743-754.
[19] DU Mingjing, DING Shifei, XUE Yu. A robust density peaks clustering algorithm using fuzzy neighborhood[J]. International journal of machine learning and cybernetics, 2018, 9(7): 1131-1140.
[20] XU Xiao, DING Shifei, SHI Zhongzhi. An improved density peaks clustering algorithm with fast finding cluster centers[J]. Knowledge-based systems, 2018, 158: 65-74.
[21] DING Shifei, DU Mingjing, SUN Tongfeng, et al. An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood[J]. Knowledge-based systems, 2017, 133: 294-313.
[22] SALVADOR S, CHAN P. Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms[C]//Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence. Boca Raton, USA, 2004.
[23] WILLIAMS C K I. Learning with kernels: support vector machines, regularization, optimization, and beyond[J]. Journal of the American statistical association, 2003, 98(462): 489.
[24] DE LIMA M D, COSTA N L, BARBOSA R. Improvements on least squares twin multi-class classification support vector machine[J]. Neurocomputing, 2018, 313: 196-205.
[25] CHEN Sugen, WU Xiaojun. Multiple birth least squares support vector machine for multi-class classification[J]. International journal of machine learning and cybernetics, 2017, 8(6): 1731-1742.

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():1121.[doi:10.11992/tis.201905025]

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

基于能量的结构化最小二乘孪生支持向量机 PDF下载HTML

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基于能量的结构化最小二乘孪生支持向量机

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