一种新的代价敏感SVDD二类分类方法

New Cost Sensitive SVDD Binary Classification Method

为提升代价敏感分类性能,通过提升较高误分代价类别的学习精度来降低总误分代价,利用支持向量域描述(Support Vector D omain Description,SVDD)实现代价敏感分类,提出一种代价敏感SVDD二类分类方法CS-SVDD。该方法首先将单类SVDD拓展为二类分类SVDD,对不同类别分别构建SVDD超球体,通过误分类代价调节SVDD分类器对不同类别样本的分类精度,对误分代价高的类别进行更为精确的学习,从而降低总误分代价;对于处于两个超球体之外或覆盖区域的类别属性不明确的样本,以误分代价最小为原则定义代价敏感决策规则。在人工数据集和UCI数据集上与同类方法进行了实验比较,实验结果表明了所提方法的有效性。

In order to improve the performance of cost-sensitive classification,this paper improves the learning accuracy of higher misclassification cost categories to reduce the total misclassification cost,uses support vector domain description(SVDD)to reali-ze cost sensitive classification,and proposes a cost sensitive SVDD two-class classification method,CS-SVDD.This method first expands single class SVDD to two class classification SVDD,and constructs SVDD hyperspheres for different categories.by adjusting the classification accuracy of SVDD classifier for different class samples through the misclassification cost,the class with high misclassification cost can be more accurately learned,so as to reduce the total misclassification cost.For the samples with ambiguous category attributes outside the two hyperspheres or in the coverage area,cost sensitive decision rules are defined based on the principle of minimum misclassification cost.Experimental results on artificial data sets and UCI data sets show the effectiveness of the proposed method.