The Divide-Conquer and Voting Strategy for Traffic Feature Selection

GAO Wen; QIAN Ya-guan; WU Chun-ming; GUO Ye; ZHU Kai; CHEN Shuang-xi

doi:10.3969/j.issn.0372-2112.2015.04.024

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The Divide-Conquer and Voting Strategy for Traffic Feature Selection

更新时间：2025-07-16

- The Divide-Conquer and Voting Strategy for Traffic Feature Selection
- Acta Electronica Sinica Vol. 43, Issue 4, Pages: 795-799(2015)
- 作者机构：
  
  1. 浙江大学计算机学院,浙江,杭州,310027
  2. 浙江科技学院理学院,浙江,杭州,310023
  3. 浙江大学图书与信息中心,浙江,杭州,310027
  4. 嘉兴职业技术学院,浙江,嘉兴,314036
  5. 浙江大学计算机学院,浙江,杭州,310027
  6. 浙江科技学院理学院,浙江,杭州,310023
  7. 浙江大学图书与信息中心,浙江,杭州,310027
  8. 嘉兴职业技术学院,浙江,嘉兴,314036
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2015.04.024
  CLC： TP393.4
- Published：2015
- 稿件说明：
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GAO Wen, QIAN Ya-guan, WU Chun-ming, et al. The Divide-Conquer and Voting Strategy for Traffic Feature Selection[J]. Acta Electronica Sinica, 2015, 43(4): 795-799.
DOI：

GAO Wen, QIAN Ya-guan, WU Chun-ming, et al. The Divide-Conquer and Voting Strategy for Traffic Feature Selection[J]. Acta Electronica Sinica, 2015, 43(4): 795-799. DOI： 10.3969/j.issn.0372-2112.2015.04.024.

摘要

特征选择作为机器学习过程中的预处理步骤

是影响分类性能的关键因素.网络流量具有数据量大

特征维度高的特点

如何快速提取特征子集

并提高分类效率对于基于机器学习的流量分类方法具有重要意义.本文提出基于分治与投票策略的特征提取方法

将数据集分裂为多个子集

分别执行特征提取算法

利用投票方法获得最后的特征子集.实验表明可有效提高特征提取的时间效率

同时使分类器取得良好的分类准确率.

Abstract

Feature selection as a substantial preprocess step is a key factor for improvement of classification accuracy.The network traffic is characterized by huge volume and high dimensions.So how to extract the optimal feature subset in short time is practical for traffic classification based on machine learning.A novel method is proposed

which partitions the traffic dataset into several small subsets

and applies special feature selection algorithm to them respectively.Finally

the optimal feature subset is obtained by voting on these alternative feature subsets.The experiment results show that the proposed method has good time efficiency in searching optimal features and helps to improve classification accuracy efficiently.

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