SpamHunting: An instance-based reasoning system for spam labelling and filtering

Decision Support Systems

Volumn 43, Issue 3, 2007, Pages 722-736

  Fdez Riverola, F ^a   Iglesias, E L ^a   Diaz F ^b   Mendez J R ^a   Corchado, J M ^c  

^a UNIVERSITY OF VIGO   (Spain)

^b UNIVERSITY OF VALLADOLID   (Spain)

^c UNIVERSITY OF SALAMANCA   (Spain)

Author keywords

Anti spam filtering; Automatic reasoning; IBR system; Model comparison

Indexed keywords

ELECTRONIC MAIL; MATHEMATICAL MODELS; MESSAGE PASSING; NONBIBLIOGRAPHIC RETRIEVAL SYSTEMS; SIGNAL FILTERING AND PREDICTION; SPAMMING;

ANTI-SPAM FILTERING; AUTOMATIC REASONING; IBR SYSTEMS; MODEL COMPARISON;

DECISION SUPPORT SYSTEMS;

EID: 33947513003     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2006.11.012     Document Type: Article

References (39)

1. Aamodt A., and Plaza E. Case-based reasoning: foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications 7 1 (1994) 39-59
2. D. Aha, Lazy learning, editor (Dordrecht, 1997).
3. Androutsopoulos I., Koutsias J., Chandrinos K.V., Paliouras G., and Spyropoulos C. An evaluation of Naïve Bayesian anti-spam filtering. Proc. of the Workshop on Machine Learning in the New Information Age, 11th European Conference on Machine Learning (Barcelona, Spain, 2000) (2000)
4. Androutsopoulos I., Paliouras G., Karkaletsis V., Sakkis G., Spyropoulos C.D., and Stamatopoulos P. Learning to filter spam e-mail: a comparison of a Naïve Bayesian and a memory-based approach. Proc. of the Workshop on Machine Learning and Textual Information Access, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (2000), Lyon, France
5. Androutsopoulos I., Paliouras G., and Michelakis E. Learning to filter unsolicited commercial e-mail. Technical Report 2004/2, NCSR "Demokritos" (2004). http://www.iit.demokritos.gr/skel/I-config/publications/ http://www.iit.demokritos.gr/skel/i-config/publications/
6. Cohen W., and Singer Y. Context-sensitive learning methods for text categorization. ACM Transactions on Information Systems 17 2 (1999) 141-173
7. Corchado E.S., Corchado J.M., and Aiken J. IBR retrieval method based on topology preserving mappings. Journal of Experimental and Theoretical Artificial Intelligence 6 3 (2004) 145-160
8. Cristianini N., and Shawe-Taylor J. An Introduction to Support Vector Machines and other Kernel-Based Learning Methods (2000), Cambridge, UK
9. Cunningham P., Nowlan N., Delany S.J., and Haahr M. A case-based approach to spam filtering that can track concept drift. Proc. of the ICCBR'03 Workshop on Long-Lived CBR Systems (Trondheim, Norway, 2003) (2003)
10. Daelemans W., Jakub Z., van der Sloot K., and van den Bosch A. TiMBL: Tilburg Memory Based Learner, Version 2.0, Reference Guide (1999), ILK, Computational Linguistics, Tilburg University. http://ilk.kub.nl/~ilk/papers/ilk9901.ps.gz http://ilk.kub.nl/~ilk/papers/ilk9901.ps.gz
11. Delany S.J., Cunningham P., and Coyle L. An assessment of case-based reasoning for spam filtering. Proc. of Fifteenth Irish Conference on Artificial Intelligence and Cognitive Science (Co. Mayo, Ireland, 2004) (2004)
12. Delany S.J., Cunningham P., Tsymbal A., and Coyle L. A case-based technique for tracking concept drift in spam filtering. Proc. of the 24th SGAI Int. Conf. on Innovative Techniques and Applications of Artificial Intelligence (2004), Cambridge, UK
13. Drucker H.D., Wu D., and Vapnik V. Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10 5 (1999) 1048-1054
14. Fallows D. Internet users and spam: what the attitudes and behavior of internet users can tell us about fighting spam. Proc. of the First Conference on Email and Anti-Spam (Mountain View, CA, 2004) (2004)
15. Fdez-Riverola F., and Corchado J.M. CBR based system for forecasting red tides. Knowledge-Based Systems 16 5-6 (2003) 321-328
16. Fdez-Riverola F., and Corchado J.M. FSfRT: forecasting system for red tides. Applied Intelligence 21 3 (2004) 251-264
17. Friedman J., Hastie T., and Tibshirani R. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28 2 (2000) 337-374
18. Gómez J.M., Puertas E., Carrero F., and De Buenaga M. Categorización de Texto Sensible al Coste Para el Filtrado de Contenidos Inapropiados en Internet. Procesamiento del Lenguaje Natural 31 (2003) 13-20
19. Gray A., and Haahr M. Personalised collaborative spam filtering. Proc. of the First Conference on Email and Anti-Spam (Mountain View, CA, 2004) (2004)
20. Hidalgo J.G., López M.M., and Sanz E.P. Combining text and heuristics for cost-sensitive spam filtering. Proc. of the 4th Computational Natural Language Learning Workshop (Lisbon, Portugal, 2000) (2000)
21. Joachims T. A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. Proc. of the 14th International Conference on Machine Learning (Nashville, TN, 1997) (1997)
22. John G., and Langley P. Estimating continuous distributions in Bayesian classifiers. Proc. of the 11th Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada, 1995) (1995)
23. Kelly M.G., Hand D.J., and Adams N.M. The impact of changing populations on classifier performance. Proc. of the 5th International Conference on Knowledge Discovery and Data Mining (San Diego, California, US, 1999) (1999)
24. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc. of the 14th International Joint Conference on Artificial Intelligence (Montreal, Canada, 1995) (1995)
25. Lenz M., Auriol E., and Manago M. Diagnosis and decision support. Lecture Notes in Artificial Intelligence 1400 (1998) 51-90
26. McSherry D. Explaining the pros and cons of conclusions in CBR. Proc. of the 7th European Conference on Case-Based Reasoning (Madrid, Spain, 2004) (2004)
27. Méndez J.R., Iglesias E.L., Fdez-Riverola F., Díaz F., and Corchado J.M. Analyzing the impact of corpus preprocessing on anti-spam filtering software. Research on Computing Science 17 (2005) 129-138
28. Postel J. On the Junk Mail Problem, RFC 706, Internet Engineering Task Force (1975). http://www.faqs.org/rfcs/rfc706.html/ http://www.faqs.org/rfcs/rfc706.html/
29. Quinlan J.R. C4.5 Programs for Machine Learning (San Mateo, CA, 1993) (1993)
30. Sahami M., Dumais S., Heckerman D., and Horvitz E. A Bayesian approach to filtering junk e-mail. Learning for Text Categorization - Papers from the AAAI Workshop (Madison, WI, 1998) (1998)
31. 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
32. Schapire R.E., and Singer Y. BoosTexter: a boosting-based system for text categorization. Machine Learning 39 2/3 (2000) 135-168
33. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys 34 1 (2002) 1-47
34. Ting K.M. Inducing cost-sensitive trees via instance weighting. Proc. of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (Nantes, France, 1998) (1998)
35. Vapnik V. The nature of statistical learning theory. Statistics for Engineering and Information Science (New York, US, 1999). 2nd Ed. (1999)
36. Watson I. Applying case-based reasoning. Techniques for Enterprise Systems (San Mateo, CA, 1997) (1997)
37. Widmer G., and Kubat M. Learning in the presence of concept drift and hidden contexts. Machine Learning 23 1 (1996) 69-101
38. Witten I.H., and Frank E. Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. (2005), Morgan Kaufmann, San Francisco
39. Yang Y., and Pedersen J.O. A comparative study on feature selection in text categorization. Proc. of the Fourteenth International Conference on Machine Learning (San Francisco, CA, 1997) (1997)