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filtering spam e-mail from mixed Arabic and English messages : A comparison of machine learning techniques
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 6, Issue 1 (31 Jan. 2009), pp.52-59, 8 p.
Publisher
Publication Date
2009-01-31
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
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.
American Psychological Association (APA)
al-Halis, Ala Mustafa Darwish. 2009. filtering spam e-mail from mixed Arabic and English messages : A comparison of machine learning techniques. The International Arab Journal of Information Technology،Vol. 6, no. 1, pp.52-59.
https://search.emarefa.net/detail/BIM-10472
Modern Language Association (MLA)
al-Halis, Ala Mustafa Darwish. filtering spam e-mail from mixed Arabic and English messages : A comparison of machine learning techniques. The International Arab Journal of Information Technology Vol. 6, no. 1 (Jan. 2009), pp.52-59.
https://search.emarefa.net/detail/BIM-10472
American Medical Association (AMA)
al-Halis, Ala Mustafa Darwish. filtering spam e-mail from mixed Arabic and English messages : A comparison of machine learning techniques. The International Arab Journal of Information Technology. 2009. Vol. 6, no. 1, pp.52-59.
https://search.emarefa.net/detail/BIM-10472
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 85-59
Record ID
BIM-10472