关键词: 样本选择, 离散度, 覆盖区域, 边界样本

Abstract:

Taking the example of designing classifier in intrusion detection system, the selection of training samples for classifier is studied. A new method is proposed for sample selection in large data set. First, it will reduce the size of selection problem via clustering, select samples according to the with-in cluster scatter value and coverage area of a sample. And it will retain boundary samples and discard most of the interior ones in each cluster. As reserving typical samples and reducing training samples, the generalization performance and training efficient of the classifier are guaranteed.

Key words: sample selection, scatter, coverage area, boundary samples