GATS-C4.5: An algorithm for optimizing features in flow classification

2008 5th IEEE Consumer Communications and Networking Conference, CCNC 2008

Volumn , Issue , 2008, Pages 466-470

  Chen, You ^a   Dai, Lei ^b   Cheng, Xue Qi ^a  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Feature selection; Flow classification; GATS C4.5

Indexed keywords

CLASSIFIERS; FEATURE EXTRACTION; FLOW PATTERNS; LEARNING SYSTEMS; OPTIMIZATION; TABU SEARCH; WELL TESTING;

BAYES METHODS; C4.5 ALGORITHM; CLASSIFICATION ACCURACIES; COMPUTATIONAL PERFORMANCE; CONSUMER COMMUNICATIONS; DATA-SETS; FEATURE SELECTION; FEATURE SELECTION ALGORITHMS; FLOW CLASSIFICATION; GATS-C4.5; KERNEL DENSITY ESTIMATION; NEGATIVE IMPACTS; ON FLOW; REDUNDANT FEATURES; RESOURCE CONSUMPTIONS; SEARCH STRATEGIES; TRAINING AND TESTING; WRAPPER APPROACHES;

CLASSIFICATION (OF INFORMATION);

EID: 51949103110     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ccnc08.2007.110     Document Type: Conference Paper

References (12)

1. T.M. Mitchell. Machine Learning. McGraw-Hill Education (ISE Edition), 1997
2. Koller, D., Sahami, M. Toward optimal feature selection. In: Proceedings of International Conference on Machine Learning. Italy,284-292,1996
3. You Chen, Yang Li, Xue-Qi Cheng and Li Guo. Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System. Conference on Information Security and Cryptology. LNCS4318, 153-167,2006
4. Nigel Williams, Sebastian Zander, Grenville Armitage. A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification. ACM SIGCOMM Computer Communication Review, vol.135, iss.5, 2006.
5. A. W. Moore, D. Zuev. Internet Traffic Classification Using Bayesian Analysis Techniques. In Proceedings of ACM SIGMETRICS, Banff, Canada, June 2005.
6. S. Zander, T.T.T. Nguyen, G. Armitage. Automated Traffic Classification and Application Identification using Machine Learning. In Proceedings of IEEE LCN, Australia, November 2005.
7. Holland J. Adaptation in natural and artificial systems. The University of Michigan Press, 1975
8. F. Glover, M. Laguna, Tabu Search, Kluwer Academic Publishers, Dordrecht, 1998.
9. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers,1993.
10. The data-sets for flow classification is available at http://www.cl.cam.ac.uk/research/srg/netos/nprobe/data/papers/sigmetric s/index.html
11. A. W. Moore, J. Hall, C. Kreibich, E. Harris, and I. Pratt. Architecture of a Network Monitor. In Passive and Active Measurement Workshop 2003 (PAM2003), La Jolla, CA, April 2003.
12. Lei Yu and Huan Liu. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 2003