基于能量的结构化最小二乘孪生支持向量机  

Energy-based structural least square twin support vector machine

作　　者：史颂辉 丁世飞[1,2] SHI Songhui;DING Shifei(School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116,China;Engineering Research Center(Ministry of Education)of Mine Digitization,Xuzhou 221116,China)

机构地区：[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

出　　处：《智能系统学报》2020年第5期1013-1019,共7页CAAI Transactions on Intelligent Systems

基　　金：国家自然科学基金项目(61672522,61976216,61379101).

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。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.

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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]