Facing the spammers: A very effective approach to avoid junk e-mails

Expert Systems with Applications

Volumn 39, Issue 7, 2012, Pages 6557-6561

  Almeida, Tiago A ^a   Yamakami, Akebo ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Confidence factors; Machine learning; Minimum description length; Spam filter; Text categorization

Indexed keywords

CONFIDENCE FACTORS; MACHINE-LEARNING; MINIMUM DESCRIPTION LENGTH; SPAM FILTER; TEXT CATEGORIZATION;

ACTIVE FILTERS; INFORMATION THEORY; INTERNET; TEXT PROCESSING;

ELECTRONIC MAIL;

EID: 84856976954     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.12.049     Document Type: Article

References (33)

1. Almeida, T.; & Yamakami, A. (2010). Content-based spam filtering. In Proceedings of the 23rd IEEE international joint conference on neural networks (pp. 1-7). Barcelona, Spain.
2. Almeida, T.; Yamakami, A.; & Almeida, J. (2009). Evaluation of approaches for dimensionality reduction applied with Naive Bayes anti-spam filters. In Proceedings of the 8th IEEE international conference on machine learning and applications (pp. 517-522). Miami, FL, USA.
3. Almeida, T.; Yamakami, A.; & Almeida, J. (2010a). Filtering spams using the minimum description length principle. In Proceedings of the 25th ACM symposium on applied computing (pp. 1856-1860). Sierre, Switzerland.
4. Almeida, T.; Yamakami, A.; & Almeida, J. (2010b). Probabilistic anti-spam filtering with dimensionality reduction. In Proceedings of the 25th acm symposium on applied computing, (pp. 1804-1808). Sierre, Switzerland.
5. T. Almeida, J. Almeida, and A. Yamakami Spam filtering: How the dimensionality reduction affects the accuracy of Naive Bayes classifiers Journal of Internet Services and Applications 1 3 2011 183 200
6. Androutsopoulos, I.; Paliouras, G.; & Michelakis, E. (2004). Learning to Filter Unsolicited Commercial E-mail. Technical Report 2004/2, National Centre for Scientific Research "Demokritos", Athens, Greece.
7. Assis, F.; Yerazunis, W.; Siefkes, C.; & Chhabra, S. (2006). Exponential differential document count - A feature selection factor for improving Bayesian filters accuracy. In Proceedings of 4th the MIT spam conference (pp. 1-6). Cambridge, MA, USA.
8. A. Barron, J. Rissanen, and B. Yu The minimum description length principle in coding and modeling IEEE Transactions on Information Theory 44 6 1998 2743 2760 (Pubitemid 128741906)
9. A. Bratko, G. Cormack, B. Filipic, T. Lynam, and B. Zupan Spam filtering using statistical data compression models Journal of Machine Learning Research 7 2006 2673 2698 (Pubitemid 46011492)
10. Breyer, L. (2005). DBACL at the TREC 2005. In Proceedings of the 14th text REtrieval conference. Gaithersburg, MD, USA.
11. Carreras, X.; & Marquez, L. (2001). Boosting trees for anti-spam email filtering. In Proceedings of the 4th international conference on recent advances in natural language processing (pp. 58-64). Tzigov Chark, Bulgaria.
12. Cormack, G. (2006). TREC 2006 Spam Track Overview.
13. G. Cormack Email spam filtering: A systematic review Foundations and Trends in Information Retrieval 1 4 2008 335 455
14. Cormack, G.; & Lynam, T. (2005). TREC 2005 Spam Track Overview.
15. G. Cormack, and T. Lynam Online supervised spam filter evaluation ACM Transactions on Information Systems 25 3 2007 1 11
16. H. Drucker, D. Wu, and V. Vapnik Support vector machines for spam categorization IEEE Transactions on Neural Networks 10 5 1999 1048 1054
17. F. Fdez-Riverola, E. Iglesias, F. Diaz, J. Mendez, and J. Corchado Applying lazy learning algorithms to tackle concept drift in spam filtering Expert Systems with Applications 33 1 2007 36 48 (Pubitemid 44959915)
18. Goodman, J.; & Yih, W.-T. (2006). Online discriminative spam filter training. In Proceedings of the 3rd conference on email and anti-spam (pp. 1-3). Mountain View, CA, USA.
19. P. Grünwald A tutorial introduction to the minimum description length principle P. Grünwald, I. Myung, M. Pitt, Advances in minimum description length: Theory and applications 2005 MIT Press 3 81
20. T. Guzella, and W. Caminhas A review of machine learning approaches to spam filtering Expert Systems with Applications 36 7 2009 10206 10222
21. Hidalgo, J. (2002). Evaluating cost-sensitive unsolicited bulk email categorization. In Proceedings of the 17th ACM symposium on applied computing (pp. 615-620). Madrid, Spain. (Pubitemid 35009659)
22. W.-F. Hsiao, and T.-M. Chang An incremental cluster-based approach to spam filtering Expert Systems with Applications 34 3 2008 1599 1608 (Pubitemid 350298190)
23. Joachims, T. (1997). A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In Proceedings of 14th international conference on machine learning (pp. 143-151). Nashville, TN, USA.
24. Kolcz, A.; & Alspector, J. (2001). SVM-based filtering of e-mail spam with content-specific misclassification costs. In Proceedings of the 1st international conference on data mining (pp. 1-14). San Jose, CA, USA.
25. G.-H. Lai, C.-M. Chen, C.-S. Laih, and T. Chen A collaborative anti-spam system Expert Systems with Applications 36 3 2009 6645 6653
26. Lynam, T.; Cormack, G.; & Cheriton, D. (2006). On-line spam filter fusion. In Proceedings of the 29th international ACM SIGIR conference (pp. 123-130). Seattle, Washington, USA.
27. C. Perlich, F. Provost, and J. Simonoff Tree induction vs. logistic regression: A learning-curve analysis Journal of Machine Learning Research 4 2003 211 255
28. J. Rissanen Modeling by shortest data description Automatica 14 1978 465 471
29. Schapire, R.; Singer, Y.; & Singhal, A. (1998). Boosting and Rocchio applied to text filtering. In Proceedings of the 21st annual international conference on information retrieval (pp. 215-223). Melbourne, Australia.
30. Sculley, D.; Wachman, G.; & Brodley, C. (2006). Spam filtering using inexact string matching in explicit feature space with on-line linear classifiers. In Proceedings of the 15th text REtrieval conference (pp. 1-10). Gaithersburg, MD, USA.
31. Siefkes, C.; Assis, F.; Chhabra, S.; & Yerazunis, W. (2004). Combining winnow and orthogonal sparse bigrams for incremental spam filtering. In Proceedings of the 8th European conference on principles and practice of knowledge discovery in databases (pp. 410-421). Pisa, Italy. (Pubitemid 39740421)
32. K.-C. Ying, S.-W. Lin, Z.-J. Lee, and Y.-T. Lin An ensemble approach applied to classify spam e-mails Expert Systems with Applications 37 3 2010
33. L. Zhang, J. Zhu, and T. Yao An evaluation of statistical spam filtering techniques ACM Transactions on Asian Language Information Processing 3 4 2004 243 269 (Pubitemid 40155221)