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Integrating Global and Local Application of Discriminative Multinomial Bayesian Classifier for Text Classification

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Intelligent Informatics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 182))

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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Author information

Authors and Affiliations

  1. Hellenic Open University, Patras, Greece

    Emmanuel Pappas

  2. Department of Mathematics, University of Patras, Patras, Greece

    Sotiris Kotsiantis

Authors
  1. Emmanuel Pappas
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  2. Sotiris Kotsiantis
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Corresponding author

Correspondence to Emmanuel Pappas .

Editor information

Editors and Affiliations

  1. (MIR Labs), Scientific Network for Innovation and, Machine Intelligence Research Labs, MIR Labs Campus, Auburn, 98071, Washington, USA

    Ajith Abraham

  2. Technology and Management, Indian Institute of Information, Technopark Campus, Trivandrum, 695581, India

    Sabu M Thampi

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32062-0

  • Online ISBN: 978-3-642-32063-7

  • eBook Packages: EngineeringEngineering (R0)

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