Combining Email models for false positive reduction

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2005, Pages 98-107

  Hershkop, Shlomo ^a   Stolfo, Salvatore J ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Aggregators; Data Mining; Email Mining; False Positive reduction; Model Combination; Multiple Classifiers; Spam

Indexed keywords

EMAIL MINING; MODEL COMBINATION; MULTIPLE CLASSIFIERS; SPAM;

ALGORITHMS; DATA MINING; DATA REDUCTION; LEARNING SYSTEMS; MATHEMATICAL MODELS;

ELECTRONIC MAIL;

EID: 32344449396     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1081870.1081885     Document Type: Conference Paper

References (43)

1. Androutsopoulos, I., Koutsias, J., Chandrinos, K., Paliouras, G. and Spyropoulos, C. An Evauation of Naïve Bayesian Anti-Spam Filtering.
2. Androutsopoulos, I., Koutsias, J., Chandrinos, K. and Spyropoulos, C., An experimental comparison of naive bayesian and keywordbased anit-spam filtering with personal email messages, in 23rd annual international ACM SIGIR conference on Research and development in information retrieval, (2000), 160-167.
3. Asker, L. and Maclin, R., Ensembles as a Sequence of Classifiers, in 15th International Joint Conference on Artificial Intelligence, (Nagoya, Japan, 1997), 860-865.
4. Bhattacharyya, M., Hershkop, S., Eskin, E. and Stolfo, S.J., MET: An Experimental System for Malicious Email Tracking, in New Security Paradigms Workshop (NSPW-2002), (Virginia Beach, VA, 2002).
5. Carreras, X. and Mrquez, L., Boosting trees for anti-spam email filtering, in RANLP-01, 4th International Conference on Recent Advances in Natural Language Processing, (Tzigov Chark, BG, 2001).
6. Clemen, R.T. Combining forecasts: A revew and annotated bibliography. International Journal of Forecasting, 5. 559 -583.
7. Cohen, W., Learning rules that classify e-mail, in Machine Learning in Information Access: AAAI Spring Symposium (SS-96-05),(1996), 18-25.
8. Damashek, M. Gauging Similarity via N-Grams: Language-Independant Sorting, Categorization and Retrieval of Text. Science, 267. 843-848.
9. Dietterich, T.G. Ensemble Methods in Machine Learning. Lecture Notes in Computer Science, 1857. 1-15.
10. Drucker, H., Wu, D. and Vapnik, V.N. Support Vector Machines for Spam Categorization. IEEE Transactions on Neural networks, 10 (5).
11. Duda, R. and Hart, P. Pattern classification and scene analysis. John Wiley & Sons, New York, 1973.
12. Graham, P. A Plan For Spam, 2003.
13. Hallam-Baker, P. A Plan For No Spam, Verisign, 2003.
14. Hershkop, S. Using URL Clustering to Classify Spam, Columbia University, 2005.
15. Hershkop, S. and Stolfo, S.J. Identifying Spam without Peeking at the Contents. ACM Crossroads.
16. Hershkop, S., Wang, K., Lee, W. and Nimeskern, O. Email Mining Toolkit Technical Manual, Computer Science Dept, Columbia University, New York, 2004.
17. Hidalgo, J.M.G. and Sanz, E.P., Combining Text and Heuristics for Cost-Sensitive Spam Filtering, in Fourth Conference on Computational Natural Language Learning and of the Second Learning Language in Logic Workshop, (Lisbon, 2000).
18. Itskevitch, J. Automatic Hierarchical E-Mail Classification Using Association Rules, 2001.
19. John, G. and Langley, P., Estimating continuous distributions in Bayesian classifiers, in Eleventh Conference on Uncertainty in Artificial Intelligence, (1995), 338-345.
20. Katirai, H. Filtering Junk E-Mail: A Performance Comparison between Genetic Programming and Naive Bayes, 1999.
21. Kiritchenko, S. and Matwin, S., Email Classification with Co-Training, in CASCON 2001, (2001).
22. Kittler, J. and Alkoot, P.M. Sum versus Vote Fusion in Multiple Classifier Systems. IEEE Transactions on Patterns Analysis and Machine Intelligence, 25 (1).
23. Kittler, J., Hatef, M., Duin, R.P.W. and Matas, J. On Combining Classifiers. IEEE Transactions on Patterns Analysis and Machine Intelligence, 20 (3).
24. Kolcz, A. and Alspector, J., SVM-based Filtering of E-mail Spam with Content-specific Misclassification Costs, in Workshop on Text Mining (TextDM'2001), (San Jose, California, 2001).
25. Larkey, L.S. and Croft, W.B., Combining Classifiers in Text Categorization. in SIGIR-96: 19th ACM International Conference on Research and Development in Information Retrieval, (Zurich, 1996), ACM Press, NY, US, 289-297.
26. Littlestone, N. and Warmuth, M.K. The Weighted Majority Algorithm. IEEE Symposium on Foundations of Computer Science.
27. Manber, U., Finding Similar Files in a Large File System, in Usenix Winter, (San Fransisco, CA, 1994), 1-10.
28. Massey, B., Thomure, M., Budrevich, R. and Long, S., Learning Spam: Simple Techniques for Freely-Available Software, in USENIX 2003, (2003).
29. Mitchel, T. Machine Learning. McGraw-Hill, 1997.
30. Peng, F. and Schuurmans, D., Combining Naive Bayes and n-Gram Language Models for Text Classi cation, in 25th European Conference on Information Retrieval Research (ECIR), (2003).
31. Pollock, S. A rule-based message filtering system. ACM Trans. Office Automation Systems, 6(3). 232-254.
32. Provost, F. and Fawcett, T. Robust Classification for Imprecise Environments. Machine Learning, 42. 203-231.
33. Provost, J. Naïve-Bayes vs. Rule-Learning in Classification of Email, 1999.
34. Rennie, J., ifile: An Application of Machine Learning to E-mail Filtering, in KDD-2000 Workshop on Text Mining, (2000).
35. Rigoutsos, I. and Huynh, T., Chung-Kwei: a Pattern-discovery-based System for the Automatic Identification of Unsolicited E-mail Messages, in ceas 2004, (Mountain View, California, 2004).
36. Sahami, M., Dumais, S., Heckerman, D. and Horvitz, E., A Bayesian approach to filtering junk e-mail, in AAAI-98 Workshop on Learning for Text Categorization, (1998).
37. Sakkis, G., Androutsopolous, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C. and Stamatopoulos, P., Stacking classifiers for Anti-Spam Filtering of Emails, in 6th conference on Empirical Methods in Natural Language Processing (EMNLP 2001), (2001).
38. Schneider, K.M., A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering, in 10th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2003), (Budapest, Hungary, 2003).
39. Segal, R.B. and Kephart, J.O., Incremental Learning in SwiftFile. in 17th International Conf. on Machine Learning, (San Francisco, CA, 2000), Morgan Kaufmann, 863-870.
40. Segal, R.B. and Kephart, J.O., MailCat: An Intelligent Assistant for Organizing E-Mail, in 3rd International Conference on Autonomous Agents, (1999).
41. Stolfo, S.J., Hershkop, S., Wang, K., Nimeskern, O. and Hu, C.-W. A Behavior-based Approach to Securing Email Systems. Mathematical Methods, Models and Architectures for Computer Networks Security.
42. Stolfo, S.J., Hershkop, S., Wang, K., Nimeskern, O. and Hu, C.-W., Behavior Profiling of Email, in 1st NSF/NIJ Symposium on Intelligence & Security Informatics(ISI 2003), (Tucson, Arizona, 2003).
43. Zheng, Z., Padmanabhan, B. and Zheng, H., A DEA Approach for Model Combination, in KDD2004, (Seattle, WA, 2004).