An AIS-based e-mail classification method

Authors:
Jinjian Qing;Ruilong Mao;Rongfang Bie;Xiao-Zhi Gao
Affiliations:
College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;College of Information Science and Technology, Beijing Normal University, Beijing, P.R. China;Department of Electrical Engineering, Helsinki University of Technology, Espoo, Finland
Venue:
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

This paper proposes a new e-mail classification method based on the Artificial Immune System (AIS), which is endowed with good diversity and self-adaptive ability by using the immune learning, immune memory, and immune recognition. In our method, the features of spam and non-spam extracted from the training sets are combined together, and the number of false positives (non-spam messages that are incorrectly classified as spam) can be reduced. The experimental results demonstrate that this method is effective in reducing the false rate.