An empirical study of three machine learning methods for spam filtering

Knowledge-Based Systems

Volumn 20, Issue 3, 2007, Pages 249-254

  Lai, Chih Chin ^a  

^a NATIONAL UNIVERSITY OF TAINAN   (Taiwan)

Author keywords

Machine learning; Spam filtering

Indexed keywords

ELECTRONIC MAIL; INTERNET; KNOWLEDGE REPRESENTATION;

SPAM FILTERING;

LEARNING SYSTEMS;

EID: 33947200129     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2006.05.016     Document Type: Article

References (16)

1. Aladdin Knowledge Systems, Anti-spam white paper, .
2. I. Androutsopoulos, J. Koutsias, K. Chandrinos, G. Paliouras, C.D. Spyropoulos, An evaluation of naive bayesian anti-spam filtering, in: Proc. of the Workshop on Machine Learning in the New Information Age: 11th European Conference on Machine Learning, 2000, pp. 9-17.
3. I. Androutsopoulos, J. Koutsias, K. Chandrinos, G. Paliouras, C.D. Spyropoulos, An experimental comparison of naive bayesian and keyword-based anti-spam filtering with encrypted personal e-mail messages, in: Proc. of the 23rd Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, 2000, pp. 160-167.
4. I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C.D. Spyropoulos, P. Stamatopoulos, Learning to filter spam e-mail: a comparison of a Naïve Bayesian and a memory-based approach, in: Proc. of the workshop: Machine Learning and Textual Information Access, 2000, pp. 1-13.
5. X. Carreras, L. Márquez, Boosting trees for anti-spam email filtering, in: Proc. of fourth Int'l Conf. on Recent Advances in Natural Language Processing, 2001, pp. 58-64.
6. W.W. Cohen, Learning rules that classify e-mail, in: Proc. of AAAI Spring Symposium on Machine Learning in Information Access, 1996, pp. 18-25.
7. Drucker H., Wu D., and Vapnik V.N. Support vector machines for spam categorization. IEEE Trans. Neural Netw. Vol. 10 No. 5 (1999) 1048-1054
8. A. Kołcz, J. Alspector, SVM-based filtering of e-mail spam with content-specific misclassification costs, in: Proc. of TextDM'01 Workshop on Text Mining, 2001.
9. M. Sahami, S. Dumais, D. Heckerman, E. Horvitz, A Bayesian approach to filtering junk e-mail, in: Learning for Text Categorization - Papers from the AAAI Workshop, 1998, pp. 55-62.
10. G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C.D. Spyropoulos, P. Stamatopoulos, Stacking classifiers for anti-spam filtering of e-mail, in: Proc. of the 6th Conf. on Empirical Methods in Natural Language Processing, 2001, pp. 44-50.
11. R. Segal, J. Crawford, J. Kephart, B. Leiba, Spamguru: an enterprise anti-spam filtering system, in: Proc. of First Conf. on Email and Anti-Spam, 2004.
12. F. Smadja, H. Tumblin, Automatic spam detection as a text classification task, in: Proc. of Workshop on Operational Text Classification Systems, 2002.
13. D.C. Trudgian, Z.R. Yang, Spam classification using nearest neighbour techniques, in: Proc. of Fifth International Conf. on Intelligent Data Engineering and Automated Learning, 2004, pp. 578-585.
14. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer
15. Yang Y. An evaluation of statistical approaches to text categorization. J. Inform. Retrieval vol. 1 (1999) 67-88
16. M. Woitaszek, M. Shaaban, R. Czernikowski, Identifying junk electronic mail in microsoft outlook with a support vector machine, in: Proc. of the 2003 Symposium on Applications and the Internet, 2003, pp. 166-169.