Rough Set Approach to Spam Filter Learning

Authors:
Mawuena Glymin;Wojciech Ziarko
Affiliations:
Department of Computer Science, University of Regina, Regina, Saskatchewan,S4S 0A2, Canada;Department of Computer Science, University of Regina, Regina, Saskatchewan,S4S 0A2, Canada
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

Citing 6
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Review:

The Knowledge Engineering Review
SDAI: An integral evaluation methodology for content-based spam filtering models

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Rough sets for spam filtering: Selecting appropriate decision rules for boundary e-mail classification

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Abstract

This article presents an elementary overview of techniques employed for spam detection via probabilistic decision table-based predictive data modelling. The focus here is to present a solution that combines simple algorithms together with some heuristics to construct generalized rough approximations of spam and legitimate e-mails using the variable precision rough set (VPRSM) approach. Experiments were conducted to explore the application of VPRSM for designing an intelligent agent for spam filtering.