A Method for the Selection of Training Samples Based on Boundary Samples
Zhang Li, Jun Feng Guo · Beijing Youdian Xueyuan xuebao · 2006
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.Experiment result shows that as reserving typical samples and reducing training samples,the generalization performance and training efficient of the classifier are guaranteed.