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Machine Learning Methods for Spam E-Mail Classification


Affiliations
1 Math. and Comp. Sci. Dept., Port Said University, Egypt
2 Inf. System Dept., Ras El Bar High Inst., Egypt
 

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.

Keywords

Spam, E-Mail Classification, Machine Learning Algorithms.
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  • Machine Learning Methods for Spam E-Mail Classification

Abstract Views: 516  |  PDF Views: 263

Authors

W. A. Awad
Math. and Comp. Sci. Dept., Port Said University, Egypt
S. M. ELseuofi
Inf. System Dept., Ras El Bar High Inst., Egypt

Abstract


The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.

Keywords


Spam, E-Mail Classification, Machine Learning Algorithms.