Abstract
Recently, many scholars make use of fusion of filters to enhance the performance of spam filtering. In the past several years, a lot of effort has been devoted to different ensemble methods to achieve better performance. In reality, how to select appropriate ensemble methods towards spam filtering is an unsolved problem. In this paper, we investigate this problem through designing a framework to compare the performances among various ensemble methods. It is helpful for researchers to fight spam email more effectively in applied systems. The experimental results indicate that online based methods perform well on accuracy, while the off-line batch methods are evidently influenced by the size of data set. When a large data set is involved, the performance of off-line batch methods is not at par with online methods, and in the framework of online methods, the performance of parallel ensemble is better when using complex filters only.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhang, L., Zhu, J., Yao, T.: An evaluation of statistical spam filtering techniques. ACM Transactions on Asian Language Information Processing (TALIP) 3(4), 243–269 (2004)
Goodman, J., Cormack, G.V., Heckerman, D.: Spam and the ongoing battle for the inbox. Communications of the ACM 50(2), 24–33 (2007)
Cormack, G.V.: Email spam filtering: A systematic review. Found. Trends Inf. Retr. 1(4), 335–455 (2007)
Yu, B., Xu, Z.b.: A comparative study for content-based dynamic spam classification using four machine learning algorithms. Knowledge-Based Systems 21(4), 355–362 (2008)
Marsono, M.N., El-Kharashi, M.W., Gebali, F.: Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification. Computer Networks 53(6), 835–848 (2009)
Guzella, T., Caminhas, W.: A review of machine learning approaches to spam filtering. Expert Systems with Applications 36(7), 10206–10222 (2009)
Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In: AAAI workshop on Learning for Text Categorization, pp. 55–62 (1998)
Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Paliouras, G., Spyropoulos, C.D.: An evaluation of naive bayesian anti-spam filtering. In: Workshop on Machine Learning in the New Information Age, June 2000, pp. 9–17 (2000)
Joachims, T.: Making large-scale support vector machine learning practical. In: Advances in kernel methods: support vector learning, pp. 169–184. MIT Press, Cambridge (1999)
Drucker, H., Vapnik, V., Wu, D.: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10, 1048–1054 (1999)
Carreras, X., Marquez, L.: Boosting trees for anti-spam email filtering. In: RANLP 2001: Proceedings of the 4th International Conference on Recent Advances in Natural Language Processing (2001)
Bratko, A., Filipič, B., Cormack, G.V., Lynam, T.R., Zupan, B.: Spam filtering using statistical data compression models. The Journal of Machine Learning Research 7, 2673–2698 (2006)
Zhou, Z.H., Wu, J., Tang, W.: Ensembling neural networks: Many could be better than all. Artificial Intelligence 137(1-2), 239–263 (2002)
Zhou, Z.H.: Ensemble learning. In: Li, S.Z. (ed.) Encyclopedia of Biometrics, Springer, Berlin (2009)
Lynam, T.R., Cormack, G.V., Cheriton, D.R.: On-line spam filter fusion. In: SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 123–130. ACM, New York (2006)
He, J., Thiesson, B.: Asymmetric gradient boosting with application to spam filtering. In: CEAS 2007: Proceedings of the Fourth Conference on Email and Anti-Spam (2007)
Liu, W., Wang, T.: Ensemble learning and active learning for spam filtering. In: TREC 2007: Proceedings of the sixteenth Text Retrieval Conference Proceedings (2007)
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Cormack, G.V., Bratko, A.: Batch and on-line spam filter comparison. In: CEAS 2006: Proceedings of the Third Conference on Email and Anti-Spam (2006)
Liu, W., Wang, T.: An ensemble learning method of multi filter for spam filtering. In: NCIRCS 2008: Proceedings of the 3rd National Conference on Information Retrieval and Content Securit. (2008)
Sculley, D., Wachman, G.M.: Relaxed online svms for spam filtering. In: SIGIR 2007: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 415–422. ACM, New York (2007)
Bratko, A., Filipič, B.: Spam filtering using compression models. Technical Report IJS-DP-9227, Department of Intelligent Systems, Jožef Stefan Institute, Ljubljana, Slovenia (2005)
Fawcett, T.: Roc graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Laboratories (2004)
Paul Komarek, A.M.: Fast robust logistic regression for large sparse datasets with binary outputs. In: Artificial Intelligence and Statistics (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, J., Gao, K., Jiao, Y., Li, G. (2009). Study on Ensemble Classification Methods towards Spam Filtering. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-03348-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03347-6
Online ISBN: 978-3-642-03348-3
eBook Packages: Computer ScienceComputer Science (R0)