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Last Modified: September 9, 2008
Citation: A. Abi-Haidar and L.M. Rocha [2008]. "Adaptive Spam Detection Inspired by a Cross-Regulation Model of Immune Dynamics: A Study of Concept Drift". In: Artificial Immune Systems: 7th International Conference, (ICARIS 2008). Bentley, Peter; Lee, Doheon; Jung, Sungwon (Eds.) Lecture Notes in Computer Science. Springer-Verlag, 5132: 36-47. doi: 10.1007/978-3-540-85072-4_4. BibTex
The pre-print is available in Adobe Acrobat (.pdf) format only. Due to mathematical notation and graphics, only the abstract is presented here.
Abstract.
This paper proposes a novel solution to spam detection inspired by a model of the adaptive immune system known as the crossregulation model. We report on the testing of a preliminary algorithm on six e-mail corpora. We also compare our results statically and dynamically with those obtained by the Naive Bayes classifier and another binary classification method we developed previously for biomedical text-mining applications. We show that the cross-regulation model is competitive against those and thus promising as a bio-inspired algorithm for spam detection in particular, and binary classification in general.
Keywords:artificial immune systems, spam detection, cross-regulation, artificial life, biologically-inspired computing, computational intelligence, machine learning.
