Abstract
In this paper a method for feature selection and classification of email spam messages is presented. The selection of features is performed in two steps: The selection is performed by measuring their entropy and a fine-tuning selection is implemented using a genetic algorithm. In the classification process, a Radial Basis Function Network is used to ensure robust classification rate even in case of complex cluster structure. The proposed method shows that, when using a two-level feature selection, a better accuracy is achieved than using one-stage selection. Also, the use of a lemmatizer or a stop-word list gives minimal classification improvement. The proposed method achieves 96-97% average accuracy when using only 20 features out of 15000.
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© 2006 Springer-Verlag Berlin Heidelberg
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Gavrilis, D., Tsoulos, I.G., Dermatas, E. (2006). Neural Recognition and Genetic Features Selection for Robust Detection of E-Mail Spam. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_54
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DOI: https://doi.org/10.1007/11752912_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34117-8
Online ISBN: 978-3-540-34118-5
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