基于杂交BPSO-SVM的网络故障特征选择

Network Fault Feature Selection Based on Breeding BPSO-SVM

为提高网络故障诊断系统的诊断精度,节约计算资源,针对需要处理的含有大量无关或冗余特征的数据,提出了一种基于杂交BPSO-SVM的网络故障特征选择算法.该算法采用封装器模式,以SVM的分类准确率和特征压缩比作为适应度函数来指导杂BPSO进行特征选择,将选择出的最优特征子集用于故障诊断.运用Kdd’99数据集的实验结果表明,杂交BPSO-SVM提高了诊断精度,降低了特征维数,可进一步提升网络故障诊断效果.

In allusion to deal particle swarm optimization with data which contains a lot of irrelevant and redundant features. A breeding binary In order to improve diagnosis support vector machines （BPSO-SVM） algorithm was proposed for feature selection. accuracy and save computing resources of the network fault diagnosis system. The algorithm adopts wrapper mode, the classification accuracy of SVM and feature compression ratio as fitness function guide the breeding BPSO algorithm to search the feature space. Finally the best fitness subset was selected out. Experimental result on KDD＇99 shows that the advanced algorithm improve the accuracy of diagnosis and reduce the feature dimension , and can further enhance network fault diagnosis effect.