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|Title||Filtering Spam E-Mail from Mixed Arabic and English Messages: A Comparison of Machine Learning Techniques.|
Spam is one of the main problems in emails communications. As the volume of non-english language spam increases, little work is done in this area. For example, in Arab world users receive spam written mostly in arabic, english or mixed Arabic and english. To filter this kind of messages, this research applied several machine learning techniques. Many researchers have used machine learning techniques to filter spam email messages. This study compared six supervised machine learning classifiers which are maximum entropy, decision trees, artificial neural nets, naïve bayes, support system machines and k-nearest neighbor. The experiments suggested that words in Arabic messages should be stemmed before applying classifier. In addition, in most cases, experiments showed that classifiers using feature selection techniques can achieve comparable or better performance than filters do not used them.
|Published in||International Arab Journal of Information Technology (IAJIT)|
|Series||Volume: 6, Number: 1|
|Item link||Item Link|
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|El-Halees, Alaa M._5.pdf||210.0Kb|