Study on ensemble classification methods towards spam filtering

Wang, Jinlong, Gao, Ke, Jiao, Yang and Li, Gang 2009, Study on ensemble classification methods towards spam filtering, Lecture notes in artificial intelligence, vol. 5678, pp. 314-325, doi: 10.1007/978-3-642-03348-3_31.

Attached Files
Name Description MIMEType Size Downloads

Title Study on ensemble classification methods towards spam filtering
Author(s) Wang, Jinlong
Gao, Ke
Jiao, Yang
Li, GangORCID iD for Li, Gang
Journal name Lecture notes in artificial intelligence
Volume number 5678
Start page 314
End page 325
Total pages 12
Publisher Springer
Place of publication Heidelberg, Germany
Publication date 2009
ISSN 0302-9743
Keyword(s) Spam email filtering
Summary Recently, many scholars make use of fusion of filters to enhance the performance of spam filtering. In the past several years, a lot of effort has been devoted to different ensemble methods to achieve better performance. In reality, how to select appropriate ensemble methods towards spam filtering is an unsolved problem. In this paper, we investigate this problem through designing a framework to compare the performances among various ensemble methods. It is helpful for researchers to fight spam email more effectively in applied systems. The experimental results indicate that online based methods perform well on accuracy, while the off-line batch methods are evidently influenced by the size of data set. When a large data set is involved, the performance of off-line batch methods is not at par with online methods, and in the framework of online methods, the performance of parallel ensemble is better when using complex filters only.
Language eng
DOI 10.1007/978-3-642-03348-3_31
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2009
Copyright notice ©2009, Springer-Verlag Berlin Heidelberg
Persistent URL

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 3 times in TR Web of Science
Scopus Citation Count Cited 4 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 708 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Wed, 05 May 2010, 14:47:39 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