Spam filtering using ML algorithms

Islam, Md. Rafiqul and Chowdhury, Morshed U. 2005, Spam filtering using ML algorithms, in Proceedings of the IADIS international conference WWW/Internet 2005, IADIS Press, Lisbon, Portugal, pp. 419-426.

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Title Spam filtering using ML algorithms
Author(s) Islam, Md. Rafiqul
Chowdhury, Morshed U.
Conference name IADIS international conference WWW/Internet (2005 : Lisbon, Portugal)
Conference location Lisbon, Portugal
Conference dates 19-22 October 2005
Title of proceedings Proceedings of the IADIS international conference WWW/Internet 2005
Editor(s) Isaias, P.
Nunes, M. B.
Rodrigues, L.
Barbosa, P.
Publication date 2005
Conference series International Association for Development of the Information Society Conference on the WWW/Internet
Start page 419
End page 426
Publisher IADIS Press
Place of publication Lisbon, Portugal
Keyword(s) spam
kernel functions
machine learning (ML)
VC dimension
Summary Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to distinguish between spam and legitimate email messages. Spam used to be considered a mere nuisance, but due to the abundant amounts of spam being sent today, it has progressed from being a nuisance to becoming a major problem. Spam filtering is able to control the problem in a variety of ways. Many researches in spam filtering has been centred on the more sophisticated classifier-related issues. Currently,  machine learning for spam classification is an important research issue at present. Support Vector Machines (SVMs) are a new learning method and achieve substantial improvements over the currently preferred methods, and behave robustly whilst tackling a variety of different learning tasks. Due to its high dimensional input, fewer irrelevant features and high accuracy, the  SVMs are more important to researchers for categorizing spam. This paper explores and identifies the use of different learning algorithms for classifying spam and legitimate messages from e-mail. A comparative analysis among the filtering techniques has also been presented in this paper.
Notes IADIS (International Association for Development of the Information Society)
ISBN 972892402X
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005, IADIS
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Document type: Conference Paper
Collection: School of Information Technology
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