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.
Attached Files
(Some files may be inaccessible until you login with your Deakin Research Online credentials)
Name
Description
MIMEType
Size
Downloads
Title
Study on ensemble classification methods towards spam filtering
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
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
890205 Information Processing Services (incl. Data Entry and Capture)