Abstract
We propose dynamic anti-spam filtering methods for agglutinative languages in general and for Turkish in particular, based on Artificial Neural Networks (ANN) and Bayesian filters. The algorithms are adaptive and have two components. The first one deals with the morphology and the second one classifies the e-mails by using the roots. Two ANN structures, single layer perceptron and multi layer perceptron, are considered and the inputs to the networks are determined using binary and probabilistic models. For Bayesian classification, three approaches are employed: binary, probabilistic, and advance probabilistic models. In the experiments, a total of 750 e-mails (410 spam and 340 normal) were used and a success rate of about 90% was achieved.
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Özgür, L., Güngör, T., Gürgen, F. (2004). Spam Mail Detection Using Artificial Neural Network and Bayesian Filter. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_74
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DOI: https://doi.org/10.1007/978-3-540-28651-6_74
Publisher Name: Springer, Berlin, Heidelberg
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