This node uses the dictionary build with the corresponding learner node and predicts the categories for uncategorized documents by looking up the relevance scores in the dictionary and assigning the categories with the highest probability.
This classifier won the first Research Garden competition where the goal was to classify product descriptions into eight different categories. See press release (on archive.org).
The complementary Naïve Bayes scoring has been described in “Tackling the Poor Assumptions of Naive Bayes Text Classifiers”; Jason D. M. Rennie; Lawrence Shih; Jaime Teevan; David R. Karger; 2003. This way, not the term counts in the regarded class, but the counts from all other classes are regarded for each class prediction. This leads to better classification accuracy.
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