Document Type

Conference Paper

Rights

This item is available under a Creative Commons License for non-commercial use only

Publication Details

In Advances in Case-Based Reasoning (Proceedings of the 7th. European Conferenc e on Case Based Reasoning, ECCBR-04), edited by P.Funk and Gonzales Calero , LNAI 3155, pp.128-141. Available from http://www.springerlink.com/content/nae85cwqh4te/?p=6442d884642041deb90342fa98da9a5fπ=0

Abstract

Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses Na¨ıve Bayes. We find that there is little to choose between the two alternatives in cross-validation tests on data sets. However, ECUE does appear to have some advantages in tracking concept drift over time.

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