基于SVM的网络数据无监督特征选择算法

Unsupervised feature selection algorithm based on support vector machine for network data

面向线性不可分的未知格式网络数据,提出了一种基于支持向量机的无监督特征选择算法。该算法通过非线性映射函数将不可分的网络数据映射到高维空间中,然后在高维空间中进行无监督的特征选择。该算法在特征选择之前不需要人工构造候选特征集合,直接从原始网络数据中自动地选择关键特征。利用人工数据集和网络数据集进行的实验结果表明:本文算法在特征选择可行性和有效性方面都有良好的表现。

Focusing on non-linear separable network data with unknown specification,an unsupervised feature selection algorithm based on Support Vector Machine（SVM） was proposed,termed UFSSVM.The proposed algorithm first maps the non-linear network data into a high dimensional feature space using a non-linear mapping function;then it performs unsupervised feature selection in the high dimensional feature space. Compared with traditional unsupervised feature selection algorithms,the proposed algorithm can automatically get the relevant features just using the original network packet without the preprocessing step to get the original feature set.The performance of the proposed algorithm is examined by simulations and with real network data set.Experiment results illustrate the feasibility and effectiveness of the proposed algorithm in feature subset selection.