USING IMMUNE ALGORITHM TO OPTIMIZE ANOMALY DETECTION BASED ON SVM

摘要：

In anomaly detection based on support vector machine,kernel parameter and error penalty c of support vector machine (SVM) determine generalization performance, and superfluous features of training samples affect classification performance. Thus, this paper presents a hybrid optimization selection method for SVM parameters and sample features using immune algorithm. Immune algorithms not only can convergence to global optimum, avoiding get in local optimum,but also can improve convergence rate. The experimental results show that our method can improve the classification accuracy and reduce the training time.

关键词： Immune algorithm support vector machine anomaly detection generalization performance affinity

作者: HONG-GANG ZHOU CHUN-DE YANG

作者单位: College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 4257-4261

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）