Abstract:

Considering the huge time overheads, low accuracy rate and undesirable classification efficiency of the conventional classification algorithms, extreme learning machine network traffic classification methods based on attribute reduction in rough set become hot methods which study network traffic classification using machine learning. Due to structural constraints, feature learning using extreme learning machine (ELM) may be ineffective for some special natural signal data. Thus, an improved hierarchical extreme learning machine algorithm based on attribute reduction in rough set (AR-HELM) is proposed as classification algorithm to construct model. The experimental results show that, comparison with traditional neural network and machine learning algorithm, the AR-HELM can be well applied to network traffic classification and improve the learning performance of the extreme learning machine. The improved algorithm model gets faster and better convergence results.

Key words: network traffic classification, attribute reduction, ELM, rough set