An approach of Bagging ensemble based on feature set and application for traffic classification

Yaguan QIAN; Xiaohui GUAN; Shuhui WU; Bensheng YUN; Dongxiao REN

doi:10.11959/j.issn.1000-0801.2018094

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An approach of Bagging ensemble based on feature set and application for traffic classification

research and development | 更新时间：2024-06-05

- An approach of Bagging ensemble based on feature set and application for traffic classification
- Telecommunications Science Vol. 34, Issue 4, Pages: 41-48(2018)
- 作者机构：
  
  1. 浙江科技学院大数据科学系，浙江杭州310023
  2. 浙江水利水电学院，浙江杭州310018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2018094
  CLC： TP301.6
- Published Online：2018-04，
  
  Published：20 April 2018
- 稿件说明：
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Yaguan QIAN, Xiaohui GUAN, Shuhui WU, et al. An approach of Bagging ensemble based on feature set and application for traffic classification[J]. Telecommunications science, 2018, 34(4): 41-48.
DOI：

Yaguan QIAN, Xiaohui GUAN, Shuhui WU, et al. An approach of Bagging ensemble based on feature set and application for traffic classification[J]. Telecommunications science, 2018, 34(4): 41-48. DOI： 10.11959/j.issn.1000-0801.2018094.

摘要

Bagging是一种经典的分类器集成方法，其有效性依赖于基分类器之间的差异度。通过遗传算法为每个基分类器构建独立的特征集，目的是获得基分类器之间更好的差异性。同时，根据不同基分类器的分类性能进行优化加权集成，获得更好的泛化能力。最后，采用Softmax回归作为基分类器，将改进的Bagging集成方法应用到互联网流量分类，实验结果表明，改进方法相比经典 Bagging 方法在分类准确率上有显著提高，与利用决策树集成的随机森林相比也有较好的性能提升。

Abstract

Bagging is a classic ensemble approach

whose effectiveness depends on the diversity of component base classifiers.In order to gain the largest diversity

employing genetic algorithms to get independent feature subset for each base classifier was proposed.Meanwhile

for better generalization

the optimal weights for the base classifiers according to their predictive performance were selected.Finally

refined Bagging ensemble based on simple Softmax regression was applied successfully in traffic classification.The experiment result shows that the proposed approach can get more improvement than the original Bagging ensemble in classification performance

and is better than the random-forests to a certain extent.

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references

ZHOU Z H . Ensemble methods:foundations and algorithms [M ] . Boca Raton : CRC PressPress , 2012 .

LI H , WANG X , DING S . Research and development of neural network ensembles:a survey [J ] . Artificial Intelligence Review , 2017 : 1 - 25 .

AMOZEGAR M , KHORASANI K . An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines [J ] . Neural Networks , 2016 ( 76 ): 106 - 121 .

INOUE H . Fast and accurate inference with adaptive ensemble prediction in image classification with deep neural networks [J ] . arXiv preprint arXiv:1702.08259 , 2017 .

WANG Q , LUO Z H , HUANG J C , et al . A novel ensemble method for imbalanced data learning:bagging of extrapolation-SMOTE SVM [J ] . Computational Intelligence and Neuroscience , 2017 ( 3 ):1827016.

高文 , 钱亚冠 , 吴春明 , 等 . 网络流量特征选择方法中的分治投票策略研究 [J ] . 电子学报 , 2015 , 43 ( 4 ): 795 - 799 .

GAO W , QIAN Y G , WU C M , et al . The divide-conquer and voting strategy for traffic feature selection [J ] . Acta Electronica Sinica , 2015 , 43 ( 4 ): 795 - 799 .

钱亚冠 , 张旻 . 基于过抽样技术的 P2P 流量识别方法 [J ] . 电信科学 , 2014 , 30 ( 4 ): 109 - 113 .

QIAN Y G , ZHANG M . P2P traffic identification based over-sampling technique [J ] . Telecommunications Science , 2014 , 30 ( 4 ): 109 - 113 .

KEARNS M . Learning Boolean formulae or finite automata is as hard as factoring [R ] . Technical Report TR-14-88 Harvard University Aiken Computation Laboratory , 1988 .

KEARNS M , VALIANT L . Cryptographic limitations on learning Boolean formulae and finite automata [J ] . Journal of the ACM (JACM) , 1994 , 41 ( 1 ): 67 - 95 .

EFRON B , TIBSHIRANI R . An introduction to the bootstrap [M ] . New York : Chapman ＆ HallPress , 1993 .

TONGAONKAR A , TORRES R , ILIOFOTOU M , et al . Towards self-adaptive network traffic classification [J ] . Computer Communications , 2015 ( 56 ): 35 - 46 .

SOYSALA M , SCHMIDT E G . Machine learning algorithms for accurate flow-based network traffic classification:evaluation and comparison [J ] . Performance Evaluation , 2010 , 67 ( 6 ): 451 - 467 .

SINGH H , . Performance analysis of unsupervised machine learning techniques for network traffic classification [C ] // 2015 Fifth International Conference on Advanced Computing＆Communication Technologies (ACCT),May 15-16,2015,Haryana,India . Piscataway:IEEE Press , 2015 : 401 - 404 .

MOORE A W . Dataset [EB ] . 2017

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