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.
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Glymin, M., Ziarko, W. (2007). Rough Set Approach to Spam Filter Learning. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_37
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DOI: https://doi.org/10.1007/978-3-540-73451-2_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73450-5
Online ISBN: 978-3-540-73451-2
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