A combined imbalanced classification approach based on D-S Evidence Theory and its application in network traffic recognition

He Hongshun; Hu Guoliang; Zhang Zhipeng; Chai Xingang; Gao Jing

doi:10.11959/j.issn.1000-0801.DXKX250278

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A combined imbalanced classification approach based on D-S Evidence Theory and its application in network traffic recognition

Research and Development | 更新时间：2026-05-13

- A combined imbalanced classification approach based on D-S Evidence Theory and its application in network traffic recognition
- Telecommunications Science Vol. 42, Issue 4, Pages: 134-149(2026)
- 作者机构：
  
  1.中国移动通信有限公司研究院，北京 100053
  2.西北农林科技大学信息工程学院，陕西杨凌 712199
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(32472002);The Natural Science Fundamental Research Program of Shaanxi Province(2023-JC-QN-0645;2023-JC-QN-0684);The Xi’an Science and Technology Planning Project(24NYGG0040)
- DOI：10.11959/j.issn.1000-0801.DXKX250278
  CLC： TN925;TP393
- Received：30 April 2025，
  
  Revised：2025-07-21，
  
  Accepted：06 August 2025，
  
  Published：20 April 2026
- 稿件说明：
移动端阅览
和红顺,胡国良,张志鹏等.一种基于证据融合的类不平衡分类方法及其在网络流量识别中的应用[J].电信科学,2026,42(04):134-149.

He Hongshun,Hu Guoliang,Zhang Zhipeng,et al.A combined imbalanced classification approach based on D-S Evidence Theory and its application in network traffic recognition[J].Telecommunications Science,2026,42(04):134-149.
和红顺,胡国良,张志鹏等.一种基于证据融合的类不平衡分类方法及其在网络流量识别中的应用[J].电信科学,2026,42(04):134-149. DOI： 10.11959/j.issn.1000-0801.DXKX250278.

He Hongshun,Hu Guoliang,Zhang Zhipeng,et al.A combined imbalanced classification approach based on D-S Evidence Theory and its application in network traffic recognition[J].Telecommunications Science,2026,42(04):134-149. DOI： 10.11959/j.issn.1000-0801.DXKX250278.

摘要

类不平衡分类问题是机器学习中常见的挑战之一，且广泛存在于网络流量识别等实际场景应用中。针对类不平衡分类问题，设计了一种基于证据理论的融合类不平衡分类算法，通过使用不同的欠采样和过采样分类算法进行建模，利用多属性决策方法将多组不同的评价输出转换成证据函数，使用证据组合规则融合得到最终的识别结果。基于人工合成数据集和UCI基准数据集，采用神经网络与随机森林分类器进行交叉验证，并应用于网络流量识别任务。实验结果表明，所提出的算法能更好地应对类不平衡分类问题，在召回率、

1-score和

-mean等评价指标上均取得显著提升。

Abstract

The imbalanced classification problem is one of the common challenges in machine learning and widely exists in practical applications such as network traffic recognition. To address this issue

a combined imbalanced classification approach based on D-S Evidence Theory was proposed. Different undersampling and oversampling classification algorithms were used for modeling

and multiple attribute decision making methods were used to convert different evaluation outputs into mass functions. Finally

evidence combination rules were used to combine the mass functions and obtain the final recognition results. Validation experiments were conducted on synthetic datasets and UCI benchmark datasets using neural network classifiers and random forest classifiers. The validated framework was then applied to real-world network traffic identification tasks. The experimental results demonstrate that the proposed approach significantly improves performance in addressing class-imbalanced cla

ssification problems

achieving notable enhancements in key evaluation metrics such as recall rate

1-score

and

-mean value.

关键词

Keywords

references

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Related Author

HE Hongshun

ZHANG Zhipeng

CHAI Xingang

GAO Jing

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Related Institution

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