用样本密度法解决支持向量机拒识区域

Sample density method for unclassifiable region of support vector machine

拒识区域是传统多分类支持向量机中存在的主要缺陷之一。为克服这一不足,提高多分类支持向量机的分类性能和泛化能力,提出将样本密度法用于解决支持向量机拒识区域问题。该方法以落入拒识区域中的样本点为中心,某一阈值为半径建立一个超球体,然后计算各类样本集在该超球体内的样本密度,最后选择最大样本密度对应的类为样本的所属类。数据实验结果表明,样本密度法实现了零拒识,有效提高了传统多分类支持向量机的分类性能。

Unclassifiable region（UR） is one of the primary disadvantages in conventional multi-classification support vector machine（MSVM）.To overcome the shortage and enhance the classification capacity and generalization ability of MSVM,a sample density method（SDM） is presented.SDM first constructs a hypersphere which considers the sample falling into the UR as a center and a certain threshold as radius.Then,sample density for each class in the hypersphere is computed and the class with the largest sample density is labelled for the sample.Experimental results on synthetic datasets and benchmark datasets show that SDM eliminates the UR in conventional MSVM and improves the classification performance of MSVM effectively.