Better Naive Bayes classification for high-precision spam detection

Software - Practice and Experience

Volumn 39, Issue 11, 2009, Pages 1003-1024

  Song, Yang ^a   Kołcz, Aleksander ^b   Gilez, C Lee ^c  

^a The Pennsylvania State University   (United States)

^b MICROSOFT   (United States)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Cascaded models; Naive Bayes; Spam filtering

Indexed keywords

AGGREGATION FUNCTIONS; CASCADED MODELS; COLLABORATIVE FILTERING; DECISION THRESHOLD; DETECTION RATES; E-MAIL SPAM; HIGH-PRECISION; INTERNET USERS; MACHINE-LEARNING; NAIVE BAYES; NAIVE BAYES CLASSIFICATION; POOR PERFORMANCE; POTENTIAL SOLUTIONS; SPAM DETECTION; SPAM FILTERING; TERM WEIGHT; TREE-BASED;

LEARNING SYSTEMS; SPAMMING;

INTERNET;

EID: 67650834914     PISSN: 00380644     EISSN: 1097024X     Source Type: Journal    
DOI: 10.1002/spe.925     Document Type: Article

References (52)

1. Lewis DD. Naive (Bayes) at forty: The independence assumption in information retrieval. Proceedings of ECML-98, 10th European Conference on Machine Learning, Chemnitz, DE, vol. 1398, Nédellec C, Rouveirol C (eds.). Springer: Heidelberg, DE, 1998; 4-15.
2. Eyheramendy S, Lewis D, Madigan D. On the Naive Bayes Model for Text Categorization, 2003; 332-339.
3. Kołcz A. Local sparsity control for naive Bayes with extreme misclassification costs. KDD '05: Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM Press: New York, NY, U.S.A., 2005; 128-137.
4. Cormack GV, Bratko A. Batch and online spam filter comparison. Conference on Email and Anti-spam, CEAS 2006, Mountain View, CA, July 2006.
5. Kołcz A, Alspector J. SVM-based filtering of e-mail spam with content-specific misclassification costs. Proceedings of the Workshop on Text Mining (TextDM'2001), San Jose, CA, U.S.A., 2001.
6. Metsis V, Androutsopoulos I, Paliouras G. Spam filtering with naive Bayes-Which naive Bayes. Proceedings of the 3rd Conference on Email and Anti-spam (CEAS-2006), Mountain View, CA, 2006.
7. Robinson G. A statistical approach to the spam problem. Linux Journal 2003; 2003(107):3.
8. Ntoulas A, Najork M, Manasse M, Fetterly D. Detecting spam web pages through content analysis. WWW '06: Proceedings of the 15th International Conference on World Wide Web. ACM Press: New York, NY, U.S.A., 2006; 83-92.
9. Yih W, Goodman J, Hulten G. Learning at low false positive rates. Proceedings of the 3rd Conference on Email and Anti-spam (CEAS-2006), Mountain View, CA, 2006.
10. Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 1997; 29(2-3):103-130.
11. McCallum A, Nigam K. A Comparison of Event Models for Naive Bayes Text Classification, 1998; 41-48.
12. Manning CD, Schütze H. Foundations of Statistical Natural Language Processing. The MIT Press: Cambridge, MA, 1999.
13. Chen SF, Goodman J. An empirical study of smoothing techniques for language modeling. Proceedings of the Thirty-fourth Annual Meeting of the Association for Computational Linguistics, Joshi A, Palmer M (eds.). Morgan Kaufmann Publishers: San Francisco, 1996; 310-318.
14. Rennie J. Improving Multi-class Text Classification with Naive Bayes, 2001.
15. Rennie J, Shih L, Teevan J, Karger D. Tackling the poor assumptions of naive Bayes text classifiers. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003). AAAI Press: Menlo Park, CA, 2003; 616-623.
16. Kim S-B, Han K-S, Rim HC, Myaeng SH. Some effective techniques for naive Bayes text classification. IEEE Transactions on Knowledge and Data Engineering 2006; 18(11):1457-1466.
17. Kołcz A, Yih WT. Raising the baseline for high-precision text classifiers. KDD '07: Proceeding of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM Press: New York, NY, U.S.A., 2007; 400-409.
18. Salton G, McGill MJ. Introduction to Modern Information Retrieval. McGraw-Hill, Inc.: New York, NY, U.S.A., 1986.
19. Debole F, Sebastiani F. Supervised term weighting for automated text categorization. SAC '03: Proceedings of the 2003 ACM Symposium on Applied Computing. ACM Press: New York, NY, U.S.A., 2003; 784-788.
20. Hall MA. Correlation-based feature selection for discrete and numeric class machine learning. ICML'00: Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann Publishers Inc.: San Francisco, CA, U.S.A., 2000; 359-366.
21. Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference (1998). Morgan Kaufmann: Los Altos, CA, 1998; 43-52.
22. Hoashi K, Matsumoto K, Inoue N, Hashimoto K. Query expansion based on predictive algorithms for collaborative filtering. SIGIR '01: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press: New York, NY, U.S.A., 2001; 414-415.
23. Song Y, Zhou D, Huang J, Councill IG, Zha H, Giles CL. Boosting the feature space: Text classification for unstructured data on the web. ICDM '06: Proceedings of the Sixth International Conference on Data Mining. IEEE Computer Society: Washington, DC, U.S.A., 2006; 1064-1069.
24. Trefethen LN, Bau D. Numerical Linear Algebra. SIAM: Philadelphia, PA, June 1997.
25. Raftery AE, Madigan D, Hoeting JA. Bayesian model averaging for linear regression models. Journal of the American Statistical Association 1997; 92(437):179-191.
26. Breiman L. Bagging predictors. Machine Learning 1996; 24(2):123-140.
27. Schapire RE, Freund Y, Bartlett P, Lee WS. Boosting the margin: A new explanation for the effectiveness of voting methods. Proceedings of the 14th International Conference on Machine Learning. Morgan Kaufmann: Los Altos, CA, 1997; 322-330.
28. Dietterich TG. Ensemble Methods in Machine Learning (Lecture Notes in Computer Science, vol. 1857). Springer: Berlin, 2000; 1-15.
29. Wu H, Phang TH, Liu B, Li X. A refinement approach to handling model misfit in text categorization. KDD '02: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: New York, NY, U.S.A., 2002; 207-216.
30. Liu B, Xia Y, Yu PS. Clustering through decision tree construction. CIKM '00: Proceedings of the Ninth International Conference on Information and Knowledge Management. ACM Press: New York, NY, U.S.A., 2000; 20-29.
31. Elkan C. The foundations of cost-sensitive learning. IJCAI, Seattle, Washington, U.S.A., 2001; 973-978.
32. Bach FR, Heckerman D, Horvitz E. Considering cost asymmetry in learning classifiers. Journal of Machine Learning Research 2006; 7:1713-1741.
33. Tang L, Liu H. Bias analysis in text classification for highly skewed data. ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining. IEEE Computer Society: Washington, DC, U.S.A., 2005; 781-784.
34. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-sampling Technique, 2002; 321-357.
35. Cormack G, Lynam T. TREC 2005 spam track overview. Proceedings of TREC 2005-The Fourteenth Text REtrieval Conference, 2005.
36. Cormack G. The TREC 2006 spam filter evaluation track. Virus Bulletin, vol. 1, 2006.
37. Blanzieri E. Instance-based spam filtering using svm nearest neighbor classifier. Proceedings of FLAIRS 2007, Key West, FL, 2007; 441-442.
38. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006; 27(8):861-874.
39. Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. COLT '92: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. ACM Press: New York, NY, U.S.A., 1992; 144-152.
40. Sculley D, Wachman GM. Relaxed online SVMs for spam filtering. SIGIR '07: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM Press: New York, NY, U.S.A., 2007; 415-422.
41. Joachims T. Training linear svms in linear time. KDD '06: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: New York, NY, U.S.A., 2006; 217-226.
42. Demšar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 2006; 7:1-30.
43. Iman R, Davenport J. Approximations of the critical region of the Friedman statistic. Communications in Statistics-Theory and Methods 1980; A9(6):571-595.
44. Dunn OJ. Multiple comparisons among means. Journal of the American Statistical Association 1961; 56:52-64.
45. Opitz D, Maclin R. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research 1999; 11:169-198.
46. Gama J, Brazdil P. Cascade generalization. Machine Learning 2000; 41(3):315-343.
47. Garcia-Pedrajas N, Ortiz-Boyer D, del Castillo-Gomariz R, Hervas-Martinez C. Cascade ensembles. Computational Intelligence and Bioinspired Systems. Springer: Berlin/Heidelberg, 2005; 598-603.
48. Zhang P, Bui TD, Suen CY. A novel cascade ensemble classifier system with a high recognition performance on handwritten digits. Pattern Recognition 2007; 40(12):3415-3429.
49. Heisele B, Serre T, Prentice S, Poggio T. Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition 2003; 36(9):2007-2017.
50. Giusti N, Masulli F, Sperduti A. Theoretical and experimental analysis of a two-stage system for classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002; 24(7):893-904.
51. Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Machine Learning 1997; 29(2-3):131-163.
52. Quinlan JR. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc.: San Francisco, CA, U.S.A., 1993.