Abstract
The Discriminative Multinomial Naive Bayes classifier has been a center of attention in the field of text classification. In this study, we attempted to increase the prediction accuracy of the Discriminative Multinomial Naive Bayes by integrating global and local application of Discriminative Multinomial Naive Bayes classifier. We performed a large-scale comparison on benchmark datasets with other state-of-the-art algorithms and the proposed methodology gave better accuracy in most cases.
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Pappas, E., Kotsiantis, S. (2013). Integrating Global and Local Application of Discriminative Multinomial Bayesian Classifier for Text Classification. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_6
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DOI: https://doi.org/10.1007/978-3-642-32063-7_6
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