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An empirical performance comparison of machine learning methods for spam e-mail categorization

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2 Author(s)
Chih-Chin Lai ; Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Tainan, Taiwan ; Ming-Chi Tsai

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning algorithms applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful information for all the machine learning algorithms considered to detect spam e-mail.

Published in:

Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on

Date of Conference:

5-8 Dec. 2004