Comparison of a SOM based sequence analysis system and Naive Bayesian classifier for spam filtering

Proceedings of the International Joint Conference on Neural Networks

Volumn 4, Issue , 2005, Pages 2571-2576

  Luo, Xiao ^a   Zincir Heywood, Nur ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; PROBLEM SOLVING; SYSTEMS ANALYSIS;

COST SCENARIOS; NAIVE BAYESIAN FILTERS; SOM BASED SEQUENCE ANALYSIS(SBSA) SYSTEM;

NEURAL NETWORKS;

EID: 33750133338     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2005.1556308     Document Type: Conference Paper

References (16)

1. I. Androutsopoulos, J. Koutsias, K. V. Chandrinos and C. D. Spyropoulos, "An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-mail Messages,' Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160-167, 2000.
2. I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C.D. Spyropoulos and P. Stamatopoulos, "Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach," Proceedings of the Workshop on Machine Learning and Textual Information Access, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 1-13, 2000.
3. G. A. Barreto and A. F R. Arujo, "Time in Self-Organizing Maps: An Overview of Models," International Journal of Computer Research, vol. 10, no. 2, pp. 139-179, 2001
4. O. A. S. Carpinteiro, "A Hierarchical Self-organizing Map Model for Sequence Recognition." Pattern Analysis Application, vol. 3, no. 3, pp. 279-287, 2000.
5. X. Carreras and L. Marquez, "Boosting Trees for Anti-Spam Email Filtering," Proceedings of the 4th International Conference on Recent Advances in Natural Language Processing, pp. 58-64, 2001.
6. W. Cohen,"Learning rules that classify e-mail," Spring Symnposium on Machine Learning in Informnation Access, 1996.
7. H. Drucker, D. Wu, and V. N. Vapnik, "Support vector machines for spam categorization," IEEE Transactions on Neural networks, vol. 10, no. 5, pp. 1048-1054, 1999
8. S. Haykin, "Neural Networks - A comprehensive foundation," Second Edition, Prentice Hall, 1999.
9. A. Kolcz and J. Alspector, "SVM-based Filtering of E-mail Spam with Content-specific Misclassification Costs," Proceedings of the ICDM-2001 Workshop on Text Mining, 2001.
10. D. Koller and M. Sahami, "Hierarchically classifying documents using very few words," Proceedings of the 14th International Conference on Machine Learning, pp. 170-178, 1997.
11. D. D. Lewis, "Naive (Bayes) at forty: The independence assumption in information retrieval," Proceedings of the 10th European Conference on Machine Learning, pp. 4-15,1998.
12. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz, "A Bayesian approach to filtering junk e-mail," Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, 1998.
13. G. Sakkis, I. Androutsopoulos, G. Paliouras, P. Stamatopoulos, "A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists," Information Retrieval, vol. 6, pp. 49-73, 2003.
14. K. Schneidera, "A comparison of event models for naive bayes anti-spam e-mail filtering," Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics, pp. 207-314, 2003.
15. F. Sebastiani, "Machine learning in automated text categorization." ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.
16. L. Zhang and T. Yao, "Filtering Junk Mail with A Maximum Entropy Model," Proceedings of the 20th International Conference on Computer Processing of Oriental Languages, pp. 446-453, 2003