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- W1553273090 abstract "Spam filtering is a text classification task to which CaseBased Reasoning (CBR) has been successfuly applied. We describe the ECUE system, which classifies emails using a feature-based form of textual CBR. Then, we describe an alternative way to compute the distances between cases in a feature-free fashion, using a distance measure based on text compression. This distance measure has the advantages of having no set-up costs and being resilient to concept drift. We report an empirical comparison, which shows the feature-free approach to be more accurate than the feature-based system. These results are fairly robust over different compression algorithms in that we find that the accuracy when using a Lempel-Ziv compressor (GZip) is approximately the same as when using a statistical compressor (PPM). We note, however, that the feature-free systems take much longer to classify emails than the feature-based system." @default.
- W1553273090 created "2016-06-24" @default.
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- W1553273090 date "2006-01-01" @default.
- W1553273090 modified "2023-09-23" @default.
- W1553273090 title "Feature based and feature free textual CBR: a comparison in spam filtering" @default.
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- W1553273090 doi "https://doi.org/10.21427/86mn-ks85" @default.
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