Aiming at problem that lack of supervised information in network traffic feature selection process

a semi-supervised network traffic feature selection algorithm based on extension of pairwise constraints is proposed.Fully considering a small mount of pairwise constraints and a large of unlabeled samples

this algorithm extends pairwise constraints sets to unlabeled samples based on inter-set and intra-set

produces more reliable pairwise constraints to reveal distribution information of the sample space.Compared with algorithms without pairwise constraints extension

experiment shows that the proposed algorithm can achieve better classification performance with fewer initial pairwise constraints.