An innovative analyser for multi-classifier e-mail classification based on grey list analysis

Authors:
Md Rafiqul Islam;Wanlei Zhou;Minyi Guo;Yang Xiang
Affiliations:
School of Engineering and Information Technology, Deakin University, Burwood, VIC 3125, Australia;School of Engineering and Information Technology, Deakin University, Burwood, VIC 3125, Australia;Department of Computer Software, The University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan;School of Management and Information Systems, Central Queensland University, QLD 4702, Australia
Venue:
Journal of Network and Computer Applications
Year:
2009

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Abstract

In this paper, we propose a new technique of e-mail classification based on the analysis of grey list (GL) from the output of an integrated model, which uses multi-classifier classification ensembles of statistical learning algorithms. The GL is the output of a list of classifiers which are not categorized as true positive (TP) nor true negative (TN) but in an unclear status. Many works have been done to filter spam from legitimate e-mails using classification algorithms and substantial performance has been achieved with some amount of false-positive (FP) tradeoffs. However, in spam filtering applications the FP problem is unacceptable in many situations, therefore it is critical to properly classify e-mails in the GL. Our proposed technique uses an innovative analyser for making decisions about the status of these e-mails. It has been shown that the performance of our proposed technique for e-mail classification is much better than the existing systems, in terms of reducing FP problems and improving accuracy.