An innovative analyser for multi-classifier e-mail classification based on grey list analysis

Journal of Network and Computer Applications

Volumn 32, Issue 2, 2009, Pages 357-366

  Islam, Md Rafiqul ^a   Zhou, Wanlei ^a   Guo, Minyi ^b   Xiang, Yang ^c  

^a DEAKIN UNIVERSITY   (Australia)

^b UNIVERSITY OF AIZU   (Japan)

^c CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

E mail; FP; GL; Spam; TN; TP

Indexed keywords

CLASSIFIERS; INTERNET; LEARNING ALGORITHMS; LEARNING SYSTEMS; SPAMMING;

E-MAIL; FP; GL; SPAM; TN; TP;

ELECTRONIC MAIL;

EID: 59249098667     PISSN: 10848045     EISSN: 10958592     Source Type: Journal    
DOI: 10.1016/j.jnca.2008.02.023     Document Type: Article

References (17)

1. Ali S., Xiang Y. Spam classification using adaptive boosting algorithm. In: Sixth IEEE/ACIS international conference on computer and information science, 2007. p. 972-6.
2. Androutsopoulos I, et al. Learning to filter unsolicited commercial email. NCRS, T. Report 2004.
3. Cheng V, Li CH. Personalized spam filtering with semi-supervised classifier ensemble. In: IEEE/WIC/ACM International Conference on Web Intelligence, 2006. p. 195-201.
4. Cristianini N., and Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods (2000), Cambridge University Press, Cambridge
5. Drucker H., Shahrary B., and Gibbon D.C. Support vector machines: relevance feedback and information retrieval. Inform Process Manage 38 3 (2002) 305-323
6. Hunt R, Carpinter J. Current and new developments in spam filtering. In: 14th IEEE international conference on networks, ICON'06, vol. 2, 2006. p. 1-6.
7. Koprinska I., Poon J., Clark J., and Chan J. Learning to classify e-mail. J Inform Sci 77 10 (2007) 2167-2187
8. Islam M., and Chowdhury M. Spam filtering using Ml algorithms. Proceedings of the IADIS international conference on WWW/Internet (2005), International Association for Development of the Information Society Press, USA 419-426
9. Islam M., and Zhou W. Architecture of adaptive spam filtering based on machine learning algorithms. In: ICA3PP 2007, Lecture Notes in Computer Science, vol. 4494 (2007), Springer, Berlin p. 458-69
10. Islam R., Chowdhury M., and Zhou W. An analysis of spam and its classification techniques based on statistical learning algorithms. Technical report TRC 05/06, Deakin University, Australia (2005)
11. Islam R., Chowdhury M., and Zhou W. An Innovative spam filtering model based on support vector machine. Proceedings of the IEEE international conference on intelligent agents, web technologies and internet commerce, vol. 2, 28-30, Austria (2005) 348-353
12. Islam R., Zhou W., and Chowdhury M. Dynamic feature selection for spam filtering using SVM. ACIS ICIS'07 (2007), IEEE 757-762
13. Kong J.S., Rezaei B.A., Sarshar N., Roychowdhury V.P., and Boykin P.O. Collaborative spam filtering using E-mail networks. J Comput 39 8 (2006) 67-73
14. Liu H., and Yu L. Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowledge Data Eng 17 4 (2005)
15. Sahami M, Dumais S, Heckerman D, Horvitz E. A Bayesian approach to filtering junk e-mail. In: Learning for text categorization: papers from the workshop, Wisconsin, Madison. AAAI technical report WS-98-05, 1998.
16. Zhang J., et al. A modified logistic regression: an approximation to SVM and its applications in large-scale text categorization. Proceedings of the 20th international conference on machine learning (2003), AAAI Press 888-895
17. Zi-Qiang Wang, Xia Sun, Xin Li, De-Xian Zhang. An efficient SVM-based spam filtering algorithm. In: International conference on machine learning and cybernetics, 2006. p. 3682-6.