Abstract
Concentration based feature construction (CFC) approach has been proposed for spam detection. In the CFC approach, Global concentration (GC) and local concentration (LC) are used independently to convert emails to 2-dimensional or 2n-dimensional feature vectors. In this paper, we propose a novel model which selects concentration construction methods adaptively according to the match between testing samples and different kinds of concentration features. By determining which concentration construction method is proper for the current sample, the email is transformed into a corresponding concentration feature vector, which will be further employed by classification techniques in order to obtain the corresponding class. The k-nearest neighbor method is introduced in experiments to evaluate the proposed concentration selection model on the classic and standard corpora, namely PU1, PU2, PU3 and PUA. Experimental results demonstrate that the model performs better than using GC or LC separately, which provides support to the effectiveness of the proposed model and endows it with application in the real world.
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References
CYREN: Internet threats trend report: April 2014. Tech. rep. (2014)
Sahami, M., Dumais, S., Heckerman, D., Horvitz, E.: A bayesian approach to filtering junk e-mail. In: Learning for Text Categorization: Papers from the 1998 Workshop, vol. 62, pp. 98–105. AAAI Technical Report WS-98-05, Madison (1998)
Ciltik, A., Gungor, T.: Time-efficient spam e-mail filtering using n-gram models. Pattern Recognition Letters 29(1), 19–33 (2008)
Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Sakkis, G., Spyropoulos, C., Stamatopoulos, P.: Learning to filter spam e-mail: A comparison of a naive bayesian and a memory-based approach. Arxiv preprint cs/0009009 (2000)
Sakkis, G., Androutsopoulos, I., Paliouras, G., Karkaletsis, V., Spyropoulos, C., Stamatopoulos, P.: A memory-based approach to anti-spam filtering for mailing lists. Information Retrieval 6(1), 49–73 (2003)
Drucker, H., Wu, D., Vapnik, V.: Support vector machines for spam categorization. IEEE Transactions on Neural Networks 10(5), 1048–1054 (1999)
Clark, J., Koprinska, I., Poon, J.: A neural network based approach to automated e-mail classification. In: Proceedings of the IEEE/WIC International Conference on Web Intelligence, WI 2003, pp. 702–705. IEEE (2003)
Wu, C.: Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Systems with Applications 36(3), 4321–4330 (2009)
Yang, Y.: Noise reduction in a statistical approach to text categorization. In: Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 256–263. ACM (1995)
Tan, Y., Deng, C., Ruan, G.: Concentration based feature construction approach for spam detection. In: International Joint Conference on Neural Networks, IJCNN 2009, pp. 3088–3093. IEEE (2009)
Ruan, G., Tan, Y.: A three-layer back-propagation neural network for spam detection using artificial immune concentration. Soft Computing 14(2), 139–150 (2010)
Zhu, Y., Tan, Y.: Extracting discriminative information from e-mail for spam detection inspired by immune system. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–7. IEEE (2010)
Zhu, Y., Tan, Y.: A local-concentration-based feature extraction approach for spam filtering. IEEE Transactions on Information Forensics and Security 6(2), 486–497 (2011)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Androutsopoulos, I., Paliouras, G., Michelakis, E.: Learning to filter unsolicited commercial e-mail. “DEMOKRITOS”. National Center for Scientific Research (2004)
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Gao, Y., Mi, G., Tan, Y. (2014). An Adaptive Concentration Selection Model for Spam Detection. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_26
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DOI: https://doi.org/10.1007/978-3-319-11857-4_26
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