Back to Search
Start Over
An empirical study of three machine learning methods for spam filtering
- Source :
-
Knowledge-Based Systems . Apr2007, Vol. 20 Issue 3, p249-254. 6p. - Publication Year :
- 2007
-
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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 09507051
- Volume :
- 20
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Knowledge-Based Systems
- Publication Type :
- Academic Journal
- Accession number :
- 24459907
- Full Text :
- https://doi.org/10.1016/j.knosys.2006.05.016