一种基于支持向量的二进制粒子群网络故障特征选择算法

网络故障诊断中大量无关或冗余的特征会降低诊断的精度,需要对初始特征进行选择。Wrapper模式特征选择方法分类算法计算量大,为了降低计算量,本文提出了基于支持向量的二进制粒子群（SVB-BPSO）的故障特征选择方法。该算法以SVM为分类器,首先通过对所有样本的SVM训练选出SV集,在封装的分类训练中仅使用SV集,然后采用异类支持向量之间的平均距离作为SVM的参数进行训练,最后根据分类结果,利用BPSO在特征空间中进行全局搜索选出最优特征集。在DARPA数据集上的实验表明本文提出的方法能够降低封装模式特征选择的计算量且获得了较高的分类精度以及较明显的降维效果。

A Support Vector Based Binary Particle Swarm Optimization Feature Selection Algorithm

In network fault diagnosis, many irrelevant and redundant features lessen the performance of diagnosis, feature selection is introduced on this condition. The wrapper feature selection algorithnas get large calculation cost, a support vector based binary particle swarm optimization（SVB-BPSO） feature selection algorithm was proposed in this paper. The support vectors（SVs） are selected from the whole datasets by SVM training, the following wrapper classification focus only on these SVs. The training parameter is decided by average distance between different class SVs. Based on the SVM classifiers, the BPSO is used for searching the whole feature space to find the best feanlre subset. Experiments on DARPA datasets show the proposed method can reduce the wrapper feature selection＇s calculation cost while gets good performance on diagnosis accuracy and dhnensional decrease.