Classification model for imbalanced traffic data based on secondary feature extraction

The non-equilibrium of network traffic data brings about the non-equilibrium of classification. Feature extraction is an effective method to reduce data dimensions, while it can intensify the influence of non-equilibrium further. A secondary feature extraction algorithm of multidimensional assessment is proposed in this study. The features of network traffic are evaluated in different dimensions to provide the basis for feature extraction. Furthermore, a model dealing with imbalanced data is proposed based on secondary feature extraction and sampling. The model combines the benefits of dimension reduction and redistribution. The experiment results show that the proposed model can not only increase classification accuracy and decrease non-equilibrium, but also enhance the performance of different classification algorithms.

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