An Empirical Performance Comparison of Machine Learning Methods for Spam E-Mail Categorization

The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam 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.

• Type: Conference
• Year: 2004
• Where: HIS
• Authors: Chih-Chin Lai, Ming-Chi Tsai
• Keywords: HIS 2004, HIS 2007, Machine Learning, Machine Learning Algorithms, Spam E-mail