用于多类别分类的一种加权超球支持向量机算法  

A Weighted Hyper-Sphere Support Vector Machine for Multi-class Classification

作　　者：刘爽[1] 陈鹏[2] 李锡祚[1] 

机构地区：[1]大连民族学院计算机科学与工程学院,辽宁大连116600 [2]大连东软信息学院计算机科学与技术系,辽宁大连116023

出　　处：《微电子学与计算机》2015年第1期19-23,28,共6页Microelectronics & Computer

基　　金：国家自然科学基金资助项目(71303031)

摘　　要：在One-Class基础上发展起来的超球支持向量机算法能有效地解决多类别分类问题.但是原始的超球支持向量机算法仍有很多需要改进的地方.经过推导和实验,得到如下结论,即超球支持向量机算法过度依赖于每个训练样本,即使该训练样本为噪音数据或是离群异常数据.因此提出在训练之前加入预处理算法,通过相似度计算删除噪音点和异常点.在训练过程中,根据公式计算每个样本的权值,区别对待每个训练样本,确保SMO求解过程迅速收敛.在测试阶段,根据测试点的位置合理选择分类规则进行正确分类.实验结果表明提出的算法可以有效减少噪音数据和异常数据对分类结果的影响,同时提高了分类精度.Based on One-Class support vector machine,Hyper-sphere support vector machine is an efficient tool to solve problems of multi-class classification.With theoretical deduction and experiments,we found that hyper-sphere support vector machine is over dependent on each training sample,even if the training sample belongs to noise or outlier.So a preprocessing method before training is proposed in this paper to remove noise and outlier by similarity computation from the original training data set.In the training process,a weight factor is added for each sample to differentiate its contribution to the resulting classifier,ensuring quick convergence of the algorithm based on SMO solving.In the testing process,an appropriate classification rule is selected by the test sample position.Experimental results compared to other hyper-sphere support vector machines,new proposed algorithm reduces influences of noises or outliers for the resulting classifier and improves classification accuracy.

关 键 词：超球支持向量机 加权 异常点 噪音 多类别分类 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]