Abstract
Because of the volume of spam email and its evolving nature, any deployed Machine Learning- based spam filtering system will need to have procedures for case-base maintenance. Key to this will be procedures to edit the case-base to remove noise and eliminate redundancy. In this paper we present a two stage process to do this. We present a new noise reduction algorithm called Blame-Based Noise Reduction that removes cases that are observed to cause misclassification. We also present an algorithm called Conservative Redundancy Reduction that is much less aggressive than the state-of-the-art alternatives and has significantly better generalisation performance in this domain. These new techniques are evaluated against the alternatives in the literature on four datasets of 1000 emails each (50% spam and 50% non spam).
This research was supported by funding from Enterprise Ireland under grant no. CFTD/03/ 219 and funding from Science Foundation Ireland under grant no. SFI-02IN.1I111.
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Delany, S.J., Cunningham, P. (2004). An Analysis of Case-Base Editing in a Spam Filtering System. In: Funk, P., González Calero, P.A. (eds) Advances in Case-Based Reasoning. ECCBR 2004. Lecture Notes in Computer Science(), vol 3155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28631-8_11
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DOI: https://doi.org/10.1007/978-3-540-28631-8_11
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