Minimizing the limitations of GL analyser of fusion based email classification

Islam, Md Rafiqul and Zhou, Wanlei 2009, Minimizing the limitations of GL analyser of fusion based email classification, Lecture notes in computer science, vol. 5574, pp. 761-774.

Attached Files
Name Description MIMEType Size Downloads

Title Minimizing the limitations of GL analyser of fusion based email classification
Author(s) Islam, Md Rafiqul
Zhou, WanleiORCID iD for Zhou, Wanlei
Journal name Lecture notes in computer science
Volume number 5574
Start page 761
End page 774
Total pages 14
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2009
ISSN 0302-9743
Keyword(s) Machine learning
Summary In the last decade, the Internet email has become one of the primary method of communication used by everyone for the exchange of ideas and information. However, in recent years, along with the rapid growth of the Internet and email, there has been a dramatic growth in spam. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs. This problem is mainly caused by the dynamic nature of spam content, spam delivery strategies, as well as the diversification of the classification algorithms. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyser as further refinements of our previous multi-classifier based email classification [10]. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique with two different variations which will analyze only the product of GL emails. Our empirical evidence proofs the improvements of this approach, in terms of complexity and cost, compared to existing GL analyser. This approach also overcomes the limitation of human interaction of existing analyzing technique.
Language eng
Field of Research 080502 Mobile Technologies
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2009
Copyright notice ©2009, Springer-Verlag Berlin Heidelberg
Persistent URL

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 1 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 572 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Fri, 28 May 2010, 15:38:04 EST by Sandra Dunoon

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact