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DPCA: A Novel Strategy to Refine the Traffic Classification Results in 5G Context

Traffic classification has been a problem that the academic community has been studying for decades. Under the background of the 5th generation mobile networks (5G), the huge amount and wide variety of traffic data have increased the difficulty of this issue. They put forward a higher demand for both the speed and accuracy of the classifier. In this paper, instead of improving the classification method, the authors propose to refine the classification result by optimizing the feature extraction process. A new extraction method named Discriminant Principal Component Analysis (DPCA) has been proposed to illustrate the effectiveness of this strategy. In the DPCA method, the authors first project the data on the direction of maximum entropy. Then, they look for the most discriminative dimensions and re-project the data onto them to extract a feature model. The extracted model has been applied to multiple classifiers for verification. Experimental results indicate that the feature model extracted by the proposed method can improve the result of traffic classification in both accuracy and running time. Proposing a feasible scheme to solve the traffic classification problem under the background of 5G network.