Deakin home > Deakin University Library > Deakin Research Online > Dynamic feature selection for spam filtering using support vector machine
Openly accessible

Dynamic feature selection for spam filtering using support vector machine

Islam, Md. Rafiqul, Zhou, Wanlei and Chowdhury, Morshed 2007, Dynamic feature selection for spam filtering using support vector machine, in 6th IEEE/ACIS International Conference on Computer and Information Science : (ICIS 2007) in conjunction with 1st IEEE/ACIS International Workshop on e-Activity (IWEA 2007) : proceedings : 11-13 July, 2007, Melbourne, Australia, IEEE Computer Society, Los Alamitos, Calif., pp. 757-762.

Attached Files (Some files may be inaccessible until you login with your Deakin Research Online credentials)
Name Description MIMEType Size Downloads
zhou-dynamicfeatureselection-2007.pdf Published version application/pdf 358.71KB 93

Title Dynamic feature selection for spam filtering using support vector machine
Author(s) Islam, Md. Rafiqul
Zhou, Wanlei
Chowdhury, Morshed
Conference name International Conference on Computer and Information Science (6th : 2007 : Melbourne, Australia)
Conference location Melbourne, Australia
Conference dates 11-13 July 2007
Title of proceedings 6th IEEE/ACIS International Conference on Computer and Information Science : (ICIS 2007) in conjunction with 1st IEEE/ACIS International Workshop on e-Activity (IWEA 2007) : proceedings : 11-13 July, 2007, Melbourne, Australia
Editor(s) Lee, Roger
Chowdhury, Morshed U.
Ray, Sid
Lee, Thuy
Publication date 2007
Conference series International Conference on Computer and Information Science
Start page 757
End page 762
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) feature extraction
information filtering
learning (artificial intelligence)
support vector machines
unsolicited e-mail
Summary Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to differentiate spam from legitimate email. Much work have been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In this paper, architecture of spam filtering has been proposed based on support vector machine (SVM,) which will get better accuracy by reducing FP problems. In this architecture an innovative technique for feature selection called dynamic feature selection (DFS) has been proposed which is enhanced the overall performance of the architecture with reduction of FP problems. The experimental result shows that the proposed technique gives better performance compare to similar existing techniques.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0769528414
9780769528410
Language eng
Field of Research 080303 Computer System Security
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008040

Document type: Conference Paper
Collections: School of Engineering and Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in Deakin Research Online is owned by the author, with all rights reserved.

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 drosupport@deakin.edu.au.

Versions
Version Filter Type
Access Statistics: 480 Abstract Views, 96 File Downloads  -  Detailed Statistics
Created: Mon, 29 Sep 2008, 09:03:50 EST