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Naive Bayes text classifiers: a locally weighted learning approach

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Due to being fast, easy to implement and relatively effective, some state-of-the-art naive Bayes text classifiers with the strong assumption of conditional independence among attributes, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, have received a great deal of attention from researchers in the domain of text classification. In this article, we revisit these naive Bayes text classifiers and empirically compare their classification performance on a large number of widely used text classification benchmark datasets. Then, we propose a locally weighted learning approach to these naive Bayes text classifiers. We call our new approach locally weighted naive Bayes text classifiers (LWNBTC). LWNBTC weakens the attribute conditional independence assumption made by these naive Bayes text classifiers by applying the locally weighted learning approach. The experimental results show that our locally weighted versions significantly outperform these state-of-the-art naive Bayes text classifiers in terms of classification accuracy.
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Keywords: complement naive Bayes; locally weighted learning; multinomial naive Bayes; naive Bayes; text classification; the one-versus-all-but-one model

Document Type: Research Article

Affiliations: 1: Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China 2: Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick E3B5A3, Canada 3: Department of Electronic Engineering, China University of Geosciences, Wuhan, Hubei 430074, China

Publication date: June 1, 2013

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