An empirical study of three machine learning methods for spam filtering
Knowledge-Based SystemsPublished 6 September 2006
Chih‐Chin Lai
Citations79
SJR quartileQ1
SJR score1.93
SNIP2.34
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TL;DR
A comparative study the performance of three commonly used machine learning methods in spam filtering and tries to integrate two spam filtering methods to obtain better performance.
Abstract
The increasing volumes of unsolicited bulk e-mail (also known as spam) are bringing more annoyance for most Internet users. Using a classifier based on a specific machine-learning technique to automatically filter out spam e-mail has drawn many researchers' attention. This paper is a comparative study the performance of three commonly used machine learning methods in spam filtering. On the other hand, we try to integrate two spam filtering methods to obtain better performance. A set of systematic experiments has been conducted with these methods which are applied to different parts of an e-mail. Experiments show that using the header only can achieve satisfactory performance, and the idea of integrating disparate methods is a promising way to fight spam.
Keywords
Computer Science
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