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Improving on the Naïve Bayes Document Classifier

The Naïve Bayes document classifier has been used in many document classification algorithms [1], but is only really useful on a small subset of documents due to it’s many shortcomings [2]. By augmenting the basic functionality of the simple Naïve Bayes classifier, the classification algorithm can be applied to a much wider range of documents. This paper investigates the advantages which can be obtained by adding Feature Selection, Binary Independence, and the Multinomial model to the Naïve Bayes classifier.