Abstract
Spam filtering is defined as a task trying to label emails with spam or ham in an online situation. The online feature requires the spam filter has a strong timely generalization and has a high processing speed. Machine learning can be employed to fulfill the two requirements. In this paper, we propose a SVMEL (SVM Ensemble Learning) method to combine five simple filters for higher accuracy and an active learning method to choose training emails for less training time. The experiments results show the filter applying active learning method can reduce requirements of labeled training emails and reach steady-state performance more quickly.
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References
Liu, W., Wang, T.: An Ensemble Learning Method of Multi-filter for Spam Filtering. In: The Third National Conference on Information Retrieval and Content Security (NCIRCS 2007), Suzhou, China (2007)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, pp. 26–29. Cambridge Univ. Press, Cambridge (2000)
Engelbrecht, A.P., et al.: Incremental Learning Using Sensitivity Analysis. In: IJCNN 1999, International. Joint Conference on Neural Networks, pp. 1350–1355 (1999)
David, D., Lewis, W.A.: A Sequential Algorithm for Training Text Classifiers. In: Proceedings of the 17the Annual ACM-SIGIR Conference, London U.K, pp. 3–12 (1994)
David, D., Lewis, C.J.: Heterogeneous Uncertainty Sampling for Supervised Learning. In: Proceedings of the Eleventh International Conference on Machine Learning, New Brunswick, NJ, pp. 48–156 (1994)
Seung, H.S., Opper, M., Somepolinsky, H.: Query by Committee. In: Proc.5th Annu. Workshop on Computation Learning Theory, pp. 287–294 (1992)
Tong, S., Koller, D.: Support Vector Machine Active Learning with Applications to Text Classification. Journal Machine Learning Research, 999–1006 (2001)
Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active Learning with Statistical Models. Artificial Intelligence Research, 129–145 (1996)
Cormack, G., Lynam, T.: TREC 2005 Spam Track Overview. University of Waterloo, Waterloo, Ontario, Canada (2005)
Cormack, G.: TREC 2006 Spam Track Overview. University of Waterloo, Waterloo, Ontario, Canada (2006)
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Liu, W., Wang, T. (2008). Active Learning for Online Spam Filtering. In: Li, H., Liu, T., Ma, WY., Sakai, T., Wong, KF., Zhou, G. (eds) Information Retrieval Technology. AIRS 2008. Lecture Notes in Computer Science, vol 4993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68636-1_63
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DOI: https://doi.org/10.1007/978-3-540-68636-1_63
Publisher Name: Springer, Berlin, Heidelberg
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