Abstract
Email has become one of the fastest and most economical forms of communication. However, the increase of email users have resulted in the dramatic increase of spam emails during the past few years. In this paper, email data was classified using four different classifiers (Neural Network, SVM classifier, Naïve Bayesian Classifier, and J48 classifier). The experiment was performed based on different data size and different feature size. The final classification result should be ‘1’ if it is finally spam, otherwise, it should be ‘0’. This paper shows that simple J48 classifier which make a binary tree, could be efficient for the dataset which could be classified as binary tree.
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Youn, S., McLeod, D. (2007). A Comparative Study for Email Classification. In: Elleithy, K. (eds) Advances and Innovations in Systems, Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6264-3_67
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DOI: https://doi.org/10.1007/978-1-4020-6264-3_67
Publisher Name: Springer, Dordrecht
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