Abstract
This paper proposes a new framework that optimizes anti-spam model with heuristic swarm intelligence optimization algorithms, and this framework could integrate various classifiers and feature extraction methods. In this framework, a swarm intelligence algorithm is utilized to optimize a parameter vector, which is composed of parameters of a feature extraction method and parameters of a classifier, considering the spam detection problem as an optimization process which aims to achieve the lowest error rate. Also, 2 experimental strategies were designed to objectively reflect the performance of the framework. Then, experiments were conducted, using the Fireworks Algorithm (FWA) as the swarm intelligence algorithm, the Local Concentration (LC) approach as the feature extraction method, and SVM as the classifier. Experimental results demonstrate that the framework improves the performance on the corpora PU1, PU2, PU3 and PUA, while the computational efficiency is applicable in real world.
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References
Commtouch,: Internet threats trend report-February 2013. Tech. rep. (2013)
Cost of spam, http://www.ferris.com/2009/01/28/cost-of-spam-is-flattening-our-2009-predictions/
Drucker, H., Wu, D., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Transactions on Neural Network 10, 1048–1054 (1999)
Ruan, G., Tan, Y.: Intelligent detection approaches for spam. In: Proceedings of International Conference on Natural Computation, pp. 1–7 (2007)
Bickel, S., Scheffer, T.: Dirichlet-enhanced spam filtering based on biased samples. Adv. Neural Inf. Process. Syst. 19, 161–168 (2007)
Kanaris, I., Kanaris, K., Houvardas, I., Stamatatos, E.: Words versus character N-grams for anti-spam filtering. Int. J. Artif. Intell. T. 16(6), 1047–1067 (2007)
Zhu, Y.C., Tan, Y.: A local-concentration-based feature extraction approach for spam filtering. IEEE Transactions on Information Forensics and Security 6(2), 1–12 (2011)
Information gain, http://en.wikipedia.org/wiki/Information_gain
Koprinska, I., Poon, J., Clark, J., Chan, J.: Learning to classify e-mail. Inform. Sci. 177, 2167–2187 (2007)
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010)
Dasgupta, D.: Advances in artificial immune systems. IEEE Computational Intelligence Magazine, 40–49 (2006)
Guzella, T.S., Caminhas, M.: A review of machine learning approaches to spam filtering. Expert Syst. Appl. 36, 10206–10222 (2009)
Blanzieri, E., Bryl, A.: A Survey of Learning-Based Techniques of e-mail Spam Filtering. Tech. Rep. 1 DIT-06-065 (2008)
Timmis, J.: Artificial immune systems—today and tomorrow. Nat. Comput., 1–18 (2007)
Oda, T., White, T.: Developing an immunity to spam. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 231–242. Springer, Heidelberg (2003)
Tan, Y., Deng, C., Ruan, G.: Concentration based feature construction approach for spam detection. In: Proceedings of International Joint Conference on Neural Networks, pp. 3088–3093 (2009)
Ruan, G., Tan, Y.: Intelligent detection approaches for spam. In: Proceedings of International Conference on Natural Computation, pp. 1–7 (2007)
Tan, Y.: Multiple-point bit mutation method of detector generation for SNSD model . In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 340–345. Springer, Heidelberg (2006)
Tan, Y., Xiao, Z.: Clonal particle swarm optimization and its applications. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 2303–2309 (2007)
Tan, Y., Wang, J.: A support vector network with hybrid kernel and minimal vapnik-chervonenkis dimension. IEEE Trans. Knowl. Data Eng. 26, 385–395 (2004)
Stuart, I., Cha, S.-H., Tappert, C.C.: A neural network classifier for junk E-mail. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 442–450. Springer, Heidelberg (2004)
Zhu, Y., Tan, Y.: A danger theory inspired learning model and its application to spam detection. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 382–389. Springer, Heidelberg (2011)
Ruan, G., Tan, Y.: A three-layer back-propagation neural network for spam detection using artificial immune concentration. Soft Comput. 14, 139–150 (2010)
Zhu, Y., Tan, Y.: Extracting discriminative information from E-mail for spam detection inspired by immune system. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 2491–2497 (2010)
Wu, C.-H.: Behavior-based spam detection using a hybrid method of rule-based techniques and neural networks. Expert Syst. Appl. 36, 4321–4330 (2009)
Siefkes, C., Assis, F., Chhabra, S., Yerazunis, W.S.: Combining winnow and orthogonal sparse bigrams for incremental spam filtering. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 410–421. Springer, Heidelberg (2004)
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He, W., Mi, G., Tan, Y. (2013). Parameter Optimization of Local-Concentration Model for Spam Detection by Using Fireworks Algorithm. In: Tan, Y., Shi, Y., Mo, H. (eds) Advances in Swarm Intelligence. ICSI 2013. Lecture Notes in Computer Science, vol 7928. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38703-6_52
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DOI: https://doi.org/10.1007/978-3-642-38703-6_52
Publisher Name: Springer, Berlin, Heidelberg
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