[1]GU Chengjie,ZHANG Shunyi,DU Anyuan.Feature selection of network traffic using a rough set and tabu search[J].CAAI Transactions on Intelligent Systems,2011,6(3):254-260.
Copy

Feature selection of network traffic using a rough set and tabu search

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 6 Number of periods: 2011 3 Page number: 254-260 Column: 学术论文—人工智能基础 Public date: 2011-06-25
Title:
Feature selection of network traffic using a rough set and tabu search
Author(s):
GU Chengjie1;  ZHANG Shunyi1;  DU Anyuan2
1.Institute of Information Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China；
2.Anhui Branch, China Mobile Group Design Institute, Hefei 230041, China
Keywords:
rough set;  tabu search;  feature selection;  network traffic
CLC:
TP391
DOI:
-
Abstract:
A feature selection of network traffic using a rough set and tabu search (RSTS) was proposed for the purpose of optimization in the feature selection of traffic classification. This approach reduced the traffic feature attribute with a rough set and established the feature subset as the initial value of a tabu search, as well as the optimal feature subset on the basis of a tabu search. The optimal feature subset with a tabu search can be selected on the basis of a feature subset. In contrast with the traditional feature selection methods based on GA and IG, RSTS was validated optimal by experimental results. It can diminish redundant feature attribution of network traffic effectively and greatly improve the classification accuracy.
References:
［1］NGUYEN T T, ARMITAGE G. A survey of techniques for internet traffic classification using machine learning［J］. IEEE Communications Surveys and Tutorials, 2008, 10(4): 5676.
［2］CALLADO A, KAMIENSKI C. A survey on internet traffic identification［J］. IEEE Communications Surveys and Tutorials, 2009, 11(3): 3752.
［3］ESTE A, GRINGOLI F, SALGARELLI L. Support vector machines for TCP traffic classification［J］. Computer Networks, 2009, 53(14): 24762490.
［4］POLAT K, GUNES S. A new feature selection method on classification of medical datasets: kernel Fscore feature selection［J］. Expert Systems with Applications, 2009, 36(7): 1036710373.
［5］PATRICIA E N, ENGELBRECHT A P. A decision rulebased method for feature selection in predictive data mining［J］. Expert Systems with Applications, 2010, 37(1): 602609.
［6］BARALDI P, PEDRONI N, ZIO E. Application of a nicked Pareto genetic algorithm for selecting features for nuclear transients classification［J］. International Journal of Intelligent Systems, 2009, 24(2): 118151.
［7］KIRA K, RENDELL L A. Feature selection problem: traditional methods and a new algorithm［C］//Proceedings of the Tenth National Conference on Artificial Intelligence. San Jose, USA, 1992: 129134.
［8］LI Yongming, ZHANG Sujuan, ZENG Xiaoping. Research of multipopulation agent genetic algorithm for feature selection［J］. Expert Systems with Applications, 2009, 36(7): 1157011581.
［9］HUANG Chenglung, DUN Jianfan. A distributed PSOSVM hybrid system with feature selection and parameter optimization［J］. Applied Soft Computing Journal, 2008, 8(4): 13811391.
［10］ZHANG Li, MENG Xiangru, WU Weijia, et al. Network fault feature selection based on adaptive immune clone selection algorithm［C］//Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization. Sanya, China, 2009: 969973.
［11］SWINIARSKI R W, SKOWRON A. Rough set methods in feature selection and recognition［J］. Pattern Recognition Letters, 2003, 24(6): 833849.
［12］殷志伟，张健沛.基于浓缩布尔矩阵的属性约简算法［J］.哈尔滨工程大学学报， 2009, 30(3)： 307311.
YIN Zhiwei, ZHANG Jianpei.An attribute reduction algorithm based on a concentration Boolean matrix［J］. Journal of Harbin Engineering University, 2009, 30(3): 307311.
［13］THANGAVEL K, PETHALAKSHMI A. Dimensionality reduction based on rough set theory: a review［J］. Applied Soft Computing Journal, 2009, 9(1): 112.
［14］WANG Yong, LI Lin, NI Jun, et al. Feature selection using tabu search with longterm memories and probabilistic neural networks［J］. Pattern Recognition Letters, 2009, 30(7): 661670.
［15］HEDAR A R, WANG J, FUKUSHIMA M. Tabu search〖LL〗for attribute reduction in rough set theory［J］. Soft Computing, 2008, 12(9): 909918.
［16］CHIOU Chiewun, WU Muhcheng. A GAtabu algorithm for scheduling inline steppers in lowyield scenarios［J］. Expert Systems with Applications, 2009, 36(9): 1192511933.
［17］MOORE A W, ZUEV D. Internet traffic classification using Bayesian analysis techniques［C］//International Conference on Measurement and Modeling of Computer Systems. New York, USA, 2005: 5060.
［18］YANG Y M, PEDERSEN J O. A comparative study on feature selection in text categorization［C］//Proceedings of the Fourteenth International Conference on Machine Learning. Nashville, USA, 1997: 412420.
Similar References:

Memo

-

Last Update: 2011-07-23

Copyright © CAAI Transactions on Intelligent Systems