Study on ensemble classification methods towards spam filtering


Autoria(s): Wang, Jinlong; Gao, Ke; Jiao, Yang; Li, Gang
Data(s)

01/01/2009

Resumo

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.<br />

Identificador

http://hdl.handle.net/10536/DRO/DU:30028510

Idioma(s)

eng

Publicador

Springer

Relação

http://dro.deakin.edu.au/eserv/DU:30028510/li-studyonensemble-2009.pdf

http://dx.doi.org/10.1007/978-3-642-03348-3_31

Direitos

2009, Springer-Verlag Berlin Heidelberg

Palavras-Chave #Spam email filtering #Ensemble #Classification
Tipo

Journal Article