关键词: 网络故障检测, 支持向量机, 单类分类, 最小二乘, 模糊隶属度

Abstract: A new classifier named Least Squares Fuzzy One Class Support Vector Machine (LSFOC-SVM) was proposed to enhance the efficiency and effect of one class support vector machine applied to network fault abnormal detection. The proposed LSFOC-SVM not only reduced the high computational cost by training with the least squares and equality constraint which obtain a set of linear equations instead of quadratic programming, but also enhanced the fault detection rate by extending the fault alarm area properly with fuzzy membership based on distance in feature space and appropriate alarm threshold. The comparative study results indicate LSFOC-SVM can improve the training efficiency greatly without affecting the diagnosis accuracy. And application tests verify the feasibility of this method.

Key words: network fault detection, Support Vector Machine (SVM), one class classification, least squares, fuzzy membership