Abstract
Spam detection has become a necessity for successful email communications, security and convenience. This paper describes a learning process where the text of incoming emails is analysed and filtered based on the salient features identified. The method described has promising results and at the same time significantly better performance than other statistical and probabilistic methods. The salient features of emails are selected automatically based on functions combining word frequency and other discriminating matrices, and emails are then encoded into a representative vector model. Several classifiers are then used for identifying spam, and self-organising maps seem to give significantly better results.
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Vrusias, B., Golledge, I. (2009). Adaptable Text Filters and Unsupervised Neural Classifiers for Spam Detection. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds) Proceedings of the International Workshop on Computational Intelligence in Security for Information Systems CISIS’08. Advances in Soft Computing, vol 53. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88181-0_25
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DOI: https://doi.org/10.1007/978-3-540-88181-0_25
Publisher Name: Springer, Berlin, Heidelberg
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