Effective spam filtering: A single-class learning and ensemble approach

Decision Support Systems

Volumn 45, Issue 3, 2008, Pages 491-503

  Wei, Chih Ping ^a   Chen, Hsueh Ching ^b   Cheng, Tsang Hsiang ^c  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b Allion Computer Inc   (Taiwan)

^c SOUTHERN TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Ensemble approach; Learning from positive and unlabeled examples; Partially supervised classification; Single class learning; Spam filtering; Text categorization

Indexed keywords

CLASSIFICATION (OF INFORMATION); ELECTRONIC MAIL; FILTRATION; INTERNET; LEARNING ALGORITHMS; TEXT PROCESSING; TURBULENT FLOW;

CLASSIFICATION MODELS; CORPORA (CO); ELSEVIER (CO); EMPIRICAL EVALUATIONS; ENSEMBLE APPROACH; INHERENT LIMITATIONS; LEARNING TECHNIQUES; NEGATIVE EXAMPLES; POSITIVE EXAMPLES; SPAM E-MAILS; SPAM FILTERING; TEXT CATEGORIZATION (TC); TWO STAGES; UNLABELED INSTANCES;

SPAMMING;

EID: 44949103122     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2007.06.010     Document Type: Article

References (47)

1. Agrawal R., Bayardo R., and Srikant R. Athena: mining-based interactive management of text databases. Proceedings of the 7th International Conference on Extending Databases Technology (EDBT00), Konstanz, Germany (2000) 365-379
2. Androutsopoulos I., Koutsias J., Chandrinos K.V., Paliouras G., and Spyropoulos C.D. An evaluation of Naive Bayesian anti-spam filtering. Proceedings of Workshop on Machine Learning in the New Information Age, Barcelona, Spain (2000) 9-17
3. Androutsopoulos I., Koutsias J., Chandrinos K.V., and Spyropoulos C.D. An experimental comparison of Naive Bayesian and keyword-based anti-spam filtering with encrypted personal e-mail messages. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Athens, Greece (2000) 160-167
4. Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36 1-2 (1999) 105-139
5. Boykin P.O., and Roychowdhury V.P. Leveraging social networks to fight spam. IEEE Computer 38 4 (2005) 61-68
6. Breiman L. Stacked regressions. Machine Learning 24 1 (1996) 49-64
7. Carreras X., and Márquez L. Boosting trees for anti-spam email filtering. Proceedings of the 4th International Conference on Recent Advances in Natural Language Processing (RANLP), Bulgaria (2001) 58-64
8. Cohen W. Learning rules that classify e-mail. Proceedings of AAAI Spring Symposium on Machine Learning in Information Access, Stanford, CA (1996) 18-25
9. Cunningham P., Nowlan N., Delany S.J., and Haahr M. A case-based approach to spam filtering that can track concept drift. Proceedings of International Conference on Case-Based Reasoning, Trondheim, Norway (June 2003)
10. De Comité F., Denis F., Gilleron R., and Letouzey F. Positive and unlabeled examples help learning. Lecture Notes in Artificial Intelligence 1720 (1999) 219-230
11. Denis F., Gilleron R., and Tommasi M. Text classification from positive and unlabeled examples. Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2002) 1927-1934
12. Dietterich T.G. Ensemble methods in machine learning. Proceedings of the First International Workshop on Multiple Classifier Systems (2000) 1-15
13. Dong Y.S., and Han K.S. A comparison of several ensemble methods for text categorization. Proceedings of 2004 IEEE International Conference on Services Computing (September 2004) 419-422
14. Drucker H., Wu D., and Vapnik V. Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10 5 (1999) 1048-1054
15. Fallows D. Spam: How It Is Hurting E-Mail and Degrading Life on the Internet. Technical Report (Pew Internet and American Life Project) (October 2003). http://www.pewinternet.org/reports/toc.asp?Report=102 available at http://www.pewinternet.org/reports/toc.asp?Report=102
16. Good I.J. The Estimation of Probabilities: an Essay on Modern Bayesian Methods (1965), MIT Press, Cambridge, MA
17. Goodman J., Cormack G.V., and Heckerman D. Spam and the ongoing battle for the inbox. Communications of the ACM 50 2 (2007) 24-33
18. Graham P. A Plan for Spam (August 2002). http://www.paulgraham.com/spam.html available at http://www.paulgraham.com/spam.html
19. Hansen L., and Salamon P. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 10 (1990) 993-1001
20. Hidalgo J.M., Bringas G.C., Sanz E.P., and Garcia F.C. Content based SMS spam filtering. Proceedings of the 2006 ACM Symposium on Document Engineering, Amsterdam, The Netherlands (2006) 107-114
21. Hovold J. Naïve Bayes spam filtering using word-position-based attributes. Proceedings of the 2nd Conference on Email and Anti-Spam, Stanford, CA (2005)
22. Joachims T. Text categorization with support vector machines: learning with many relevant features. Proceedings of the 10th European Conference on Machine Learning (ECML 98), Chemnitz, Germany (1998) 137-142
23. Kolcz A., and Alspector J. SVM-based filtering of email spam with content-specific misclassification costs. Proceedings of Workshop on Text Mining, Chicago, IL (2001)
24. Larkey L., and Croft W. Combining classifiers in text categorization. Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1996), Zurich, Switzerland 289-297
25. Letouzey F., Denis F., and Gilleron R. Learning from positive and unlabeled examples. Proceedings of the 11th International Conference on Algorithmic Learning Theory, Sydney, Australia (December 2000) 71-85
26. Li X., and Liu B. Learning to classify text using positive and unlabeled data. Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, Mexico (August 2003) 587-594
27. Liu B., Lee W.S., Yu P.S., and Li X. Partially supervised classification of text documents. Proceedings of the 19th International Conference on Machine Learning (ICML-2002), Sydney, Australia (July 2002) 387-394
28. Masand B., Linoff G., and Waltz D. Classifying news stories using memory based reasoning. Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Copenhagen, Denmark (1992) 59-65
29. Messaging Anti-Abuse Working Group, MAAWG Email Metrics Program, First Quarter 2006 Report, June 2006, available at www.maawg.org/about/FINAL_1Q2006_Metrics_Report.pdf.
30. Metsis V., Androutsopoulos I., and Paliouras G. Spam filtering with Naïve Bayes-which Naïve Bayes?. Proceedings of the 3rd Conference on Email and Anti-Spam, Mountain View, CA (July 2006)
31. Opitz D., and Maclin R. Popular ensemble methods: an empirical study. Journal of Artificial Intelligence Research 11 (1999) 169-198
32. Pantel P., and Lin D. SpamCop: a spam classification and organization program. Proceedings of 1998 Workshop on Learning for Text Categorization, Madison, WI (1998)
33. Porter M.F. An Algorithm for Suffix Stripping. Program 14 3 (1980) 130-137. http://www.tartarus.org/~martin/PorterStemmer/ (Porter Stemming Algorithm also available at http://www.tartarus.org/~martin/PorterStemmer/.)
34. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, San Mateo, CA
35. Sahami M., Dumais S., Heckerman D., and Horvitz E. A Bayesian approach to filtering junk e-mail. Proceedings of 1998 Workshop on Learning for Text Categorization, Madison, WI (1998)
36. Sakkis G., Androutsopoulos I., Paliouras G., Karkaletsis V., Spyropoulos C., and Stamatopoulos P. A memory-based approach to anti-spam filtering for mailing lists. Information Retrieval 6 1 (2003) 49-73
37. Schneider K.M. A comparison of event models for Naïve Bayes anti-spam e-mail filtering. Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, Budapest, Hungary (2003) 307-314
38. Schneider K.M. On word frequency information and negative evidence in Naïve Bayes text classification. Proceedings of the 4th International Conference on Advances in Natural Language Processing, Alicante, Spain (2004) 474-485
39. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 34 1 (2002) 1-47
40. Taylor C. Spam's big bang. Time (June 16, 2003) 51
41. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York, NY
42. Weiss S.M., Apte C., Damerau F.J., Johnson D.E., Oles F.J., Goetz T., and Hampp T. Maximizing text-mining performance. IEEE Intelligence Systems 14 4 (1999) 63-69
43. Whitworth B., and Whitworth E. Spam and the social-technical gap. IEEE Computer 37 10 (2004) 38-45
44. Yu H., Han J., and Chang K.C.C. PEBL: web page classification without negative examples. IEEE Transaction on Knowledge and Data Engineering 16 1 (2004) 70-81
45. Zhang L., and Yao T. Filtering junk mail with a maximum entropy model. Proceedings of the 20th International Conference on Computer Processing of Oriental Languages (ICCPOL 03) (2003) 446-453
46. Zhang L., Zhu J., and Yao T. An evaluation of statistical spam filtering techniques. ACM Transactions on Asian Language Information Processing 3 4 (2004) 243-269
47. Zorkadis V., Panayotou M., and Karras D.A. Improved spam e-mail filtering based on committee machines and information theoretic feature extraction. Proceedings of International Joint Conference on Neural Networks, Montreal, Canada (2005) 179-184