密度惩罚支持向量数据描述

Density-Punished Support Vector Data Description

基于相对密度概念,文中提出一种密度惩罚的支持向量数据描述方法.该方法把相对密度和对样本的误分惩罚关联起来.如果样本的相对密度较大,则其是目标样本的可能性较大,因此需加大其误分的惩罚力度.同理相对密度小的样本,有可能是位于边界上的点或噪声点,相应的误分惩罚应减小.在UCI数据集上的实验结果表明,文中方法比标准支持向量数据描述及密度诱导的支持向量数据描述都有更好的描述性能.

Based on the concept of relative density degrees, a density-punished support vector data description method is presented. The relative density degrees are associated with punishing misclassifications. If the relative density degree of the sample is large, it is likely to be a target sample. Thus, a large penalty should be put on its misclassification. Similarly, if the relative density degree of the sample is small, it might be a boundary or noise point so that the corresponding penalty for its misclassification should be small as well. The experimental results on UCI datasets show that the proposed method has better performance compared with support vector data description and density-induced support vector data description.