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Study on ensemble classification methods towards spam filtering

journal contribution
posted on 2009-01-01, 00:00 authored by J Wang, K Gao, Y Jiao, Gang LiGang Li
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.

History

Journal

Lecture notes in artificial intelligence

Volume

5678

Pagination

314 - 325

Publisher

Springer

Location

Heidelberg, Germany

ISSN

0302-9743

eISSN

1611-3349

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2009, Springer-Verlag Berlin Heidelberg

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