Stacking classifiers for anti-spam filtering of e-mail

Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing, EMNLP 2001

Volumn , Issue , 2001, Pages

  Sakkis, Georgios ^a   Androutsopoulos, Ion ^b   Paliouras, Georgios ^b   Karkaletsis, Vangelis ^b   Spyropoulos, Constantine D ^b   Stamatopoulos, Panagiotis ^a  

^a UNIVERSITY OF ATHENS   (Greece)

^b INSTITUTE OF INFORMATICS AND TELECOMMUNICATIONS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRONIC MAIL; NATURAL LANGUAGE PROCESSING SYSTEMS;

ANTI-SPAM FILTERING; ANTI-SPAM FILTERS; COMBINING CLASSIFIERS; COST-SENSITIVE APPLICATIONS; REAL-LIFE APPLICATIONS; STACKED GENERALIZATION; STACKINGS; TEXT CATEGORIZATION;

TEXT PROCESSING;

EID: 85126956858     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Aha, W. D., Kibler D., and Albert, M.K., (1991) Instance-Based Learning Algorithms. “Machine Learning”, Vol. 6, pp. 37-66.
2. Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., and Spyropoulos, C.D. (2000a) “An evaluation of naïve Bayesian anti-spam filtering”. In Proceedings of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning (ECML 2000), Barcelona, Spain, pp. 9-17.
3. Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C.D., and Stamatopoulos, P. (2000b). “Learning to filter spam e-mail: a comparison of a naïve Bayesian and a memory-based approach”. In Proceedings of the Workshop on Machine Learning and Textual Information Access, PKDD 2000, Lyon, France, pp. 1- 3.
4. Androutsopoulos, I, Koutsias, J, Chandrinos, K.V., and Spyropoulos, C.D. (2000c) “An experimental comparison of naïve Bayesian and keyword-based anti-spam filtering with encrypted personal e-mail messages”. In Proceedings of SIGIR 2000, Athens, Greece, pp. 160-167.
5. Cranor, L.F., and LaMacchia, B.A. (1998). “Spam!”, Communications of ACM, 41(8):74-83.
6. Daelemans, W., Zavrel, J., van der Sloot, K., and van den Bosch, A. (2000) TiMBL: Tilburg Memory Based Learner, version 3.0, Reference Guide. ILK, Computational Linguistics, Tilburg University. http:/ilk.kub.nl/~ilk/papers.
7. Dietterich, G. T. (1997). “Machine Learning Research: Four Current Directions”. AI Magazine 18(4):97-136.
8. Drucker, H. D.,Wu, D., and Vapnik V. (1999). “Support Vector Machines for Spam Categorization”. IEEE Transactions On Neural Networks, 10(5).
9. Duda, R.O, and Hart, P.E. (1973). “Bayes decision theory”. Chapter 2 in Pattern Classification and Scene Analysis, pp. 10-43, John Wiley.
10. Dudani, A. S. (1976). “The distance-weighted k-nearest neighbor rule”. IEEE Transactions on Systems, Man and Cybernetics, 6(4):325-327.
11. th Computational Natural Language Learning Workshop, CoNLL-2000, Lisbon, Portugal, pp. 99-102.
12. th International Joint Conference on Artificial Intelligence (IJCAI-1995), Morgan Kaufmann, pp. 1137-1143.
13. Mitchell, T.M. (1997). Machine Learning. McGrawHill.
14. Pantel, P., and Lin, D. (1998). “SpamCop: a spam classification and organization program”. In Learning for Text Categorization - Papers from the AAAI Workshop, pp. 95-98, Madison Wisconsin. AAAI Technical Report WS-98-05.
15. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, California.
16. Sahami, M., Dumais, S., Heckerman D., and Horvitz, E. (1998). “A Bayesian approach to filtering junk e-mail”. In Learning for Text Categorization - Papers from the AAAI Workshop, pp. 55-62, Madison Wisconsin. AAAI Technical Report WS-98-05.
17. Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C.D., and Stamatopoulos, P. (2001) “A memory-based approach to anti-spam filtering”. NCSR “Demokritos” Technical Report, Athens, Greece.
18. Schapire, R.E., and Singer, Y. (2000). “BoosTexter: a boosting-based system for text categorization”. Machine Learning, 39(2/3):135-168.
19. Sebastiani, F. (2001). Machine Learning in Automated Text Categorization. Revised version of Technical Report IEI-B4-31-1999, Istituto di Elaborazione dell'Informazione, Consiglio Nazionale delle Ricerche, Pisa, Italy.
20. Wettschereck, D., Aha, W. D., and Mohri, T. (1995). A Review and Comparative Evaluation of Feature Weighting Methods for Lazy Learning Algorithms. Technical Report AIC-95-012, Naval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence, Washington, D.C.
21. Wolpert, D. (1992). “Stacked Generalization”. Neural Networks, 5(2):241-260.