Raising the Baseline for High-Precision Text Classifiers

Published in KDD-2007, 2007

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Many important application areas of text classifiers demand high precision and it is common to compare prospective solutions to the performance of Naive Bayes. This baseline is usually easy to improve upon, but in this work we demonstrate that appropriate document representation can make outperforming this classifier much more challenging. Most importantly, we provide a link between Naive Bayes and the logarithmic opinion pooling of the mixture-of-experts framework, which dictates a particular type of document length normalization. Motivated by document-specific feature selection we propose monotonic constraints on document term weighting, which is shown as an effective method of fine-tuning document representation. The discussion is supported by experiments using three large email corpora corresponding to the problem of spam detection, where high precision is of particular importance.