基于正则化技术的对支持向量机特征选择算法

对支持向量机(twin support vector machine,TWSVM)的优化思想源于基于广义特征值近似支持向量机(proxi mal SVMbased on generalized eigenvalues,GEPSVM),问题解归结为求解两个SVM型问题,因此,计算开销缩减到标准SVM的1/4.除了保留了GEPSVM优势外,在分类性能上TWSVM远优于GEPSVM,但仍需求解凸规划问题,并且,目前尚无有效的TWSVM的特征提取算法提出.首先,向TWSVM模型中引入正则项,提出了正则化TWSVM(RTWSVM).与TWSVM不同,RTWSVM保证了该问题为一个强凸规划问题.在此基础上,构造了TWSVM的特征提取算法(FRTWSVM).该分类器只需求解一个线性方程系统,无需任何凸规划软件包.在保证得到与TWSVM相当的分类性能以及较快的计算速度上,此方式还减少了输入空间的特征数.对于非线性问题,FRTWSVM可以减少核函数数目.

A Feature Selection Method for TWSVM via a Regularization Technique

Twin support vector machine(TWSVM for short),as a variant of GEPSVM,is based on the idea of generalized support vector machine(GEPSVM),which determines two nonparallel planes by solving two related SVM-type problems,such that its computing cost in the training phase is 1 4 of standard SVM.In addition to keeping the superior characteristics of GEPSVM,classification performance of TWSVM significantly outperforms that of GEPSVM.However,the stand-alone method requires the solution of two smaller quadratic progr...