Neural recognition and genetic features selection for robust detection of e-mail spam

Authors:
Dimitris Gavrilis;Ioannis G. Tsoulos;Evangelos Dermatas
Affiliations:
Electrical & Computer Engineering, University of Patras;Computer Science Department, University of Ioannina;Electrical & Computer Engineering, University of Patras
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

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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.