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Improving Naive Bayes Text Classifier Using Smoothing Methods

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Advances in Information Retrieval (ECIR 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4425))

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Abstract

The performance of naive Bayes text classifier is greatly influenced by parameter estimation, while the large vocabulary and scarce labeled training set bring difficulty in parameter estimation. In this paper, several smoothing methods are introduced to estimate parameters in naive Bayes text classifier. The proposed approaches can achieve better and more stable performance than Laplace smoothing.

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Authors and Affiliations

  1. Dept. of Electronic Engineering, Tsinghua University, 100084, Beijing, China, State Key Laboratory of Intelligent Technology and Systems,  

    Feng He & Xiaoqing Ding

Authors
  1. Feng He
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  2. Xiaoqing Ding
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Editor information

Giambattista Amati Claudio Carpineto Giovanni Romano

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© 2007 Springer Berlin Heidelberg

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He, F., Ding, X. (2007). Improving Naive Bayes Text Classifier Using Smoothing Methods. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_73

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  • DOI: https://doi.org/10.1007/978-3-540-71496-5_73

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71494-1

  • Online ISBN: 978-3-540-71496-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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