Feature Selection Algorithm for Class Imbalanced Internet Traffic

Hong TANG; Dan LIU; LiShuang YAO; Yunfeng WANG; Zuofei PEI

doi:10.11999/JEIT190992

Volume 43 Issue 4

Apr. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(4): 923-930

Hong TANG, Dan LIU, LiShuang YAO, Yunfeng WANG, Zuofei PEI. Feature Selection Algorithm for Class Imbalanced Internet Traffic[J]. Journal of Electronics & Information Technology, 2021, 43(4): 923-930. doi: 10.11999/JEIT190992

Citation:

Hong TANG, Dan LIU, LiShuang YAO, Yunfeng WANG, Zuofei PEI. Feature Selection Algorithm for Class Imbalanced Internet Traffic[J]. Journal of Electronics & Information Technology, 2021, 43(4): 923-930. doi: 10.11999/JEIT190992

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Feature Selection Algorithm for Class Imbalanced Internet Traffic

doi: 10.11999/JEIT190992

Hong TANG^{1, 2},
Dan LIU^{1, 2
,
,},
LiShuang YAO^{1, 2},
Yunfeng WANG¹,
Zuofei PEI^{1, 2}

1.
School of Communication and Information Engineering, Chongqing University of Posts and Communications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Communications, Chongqing 400065, China

Funds: Changjiang Scholars and Innovative Research Team in University (IRT_16R72)

Received Date: 2019-12-11
Rev Recd Date: 2021-02-22

Available Online: 2021-03-04

Publish Date: 2021-04-20

Abstract

Abstract

Class imbalance always exists in the process of network traffic classification. Considering the problem, a new feature selection algorithm using Weighted Symmetric Uncertainty (WSU) and Approximate Markov Blanket (AMB) is proposed. Firstly, a feature metric is defined using category distribution information, which is biased to minority classes. This makes it easier pick out features which have strong correlation with minority classes. Then, considering the correlation between features and categories and between features and features, the weighted symmetry uncertainty and approximate Markov blanket are used to delete the unrelated features and redundant features. Finally, the feature dimension is further reduced to determine the optimal feature subset, by using feature evaluation functions based on correlation measures and sequence search algorithms. The experimental results demonstrate that the algorithm can effectively improve the classification performance of minority classes without sacrificing the accuracy of the overall classification.
- Traffic classification,
- Feature selection,
- Class imbalance,
- Weighted Symmetric Uncertainty (WSU),
- Approximate Markov Blanket (AMB)

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References(23)

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