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A Generalization of Independence in Naive Bayes Model

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Intelligent Data Engineering and Automated Learning – IDEAL 2010 (IDEAL 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6283))

Abstract

In this paper, generalized statistical independence is proposed from the viewpoint of generalized multiplication characterized by a monotonically increasing function and its inverse function, and it is implemented in naive Bayes models. This paper also proposes an idea of their estimation method which directly uses empirical marginal distributions to retain simplicity of calculation. Our method is interpreted as an optimization of a rough approximation of the Bregman divergence so that it is expected to have a kind of robust property. Effectiveness of our proposed models is shown by numerical experiments on some benchmark data sets.

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Author information

Authors and Affiliations

  1. Aoyama Gakuin University, 5-10-1 Fuchinobe, Chuo, Sagamihara, Kanagawa, 252-5258, Japan

    Yu Fujimoto

  2. Waseda University, 3-4-1 Ohkubo, Shinjuku, Tokyo, 169-8555, Japan

    Noboru Murata

Authors
  1. Yu Fujimoto
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  2. Noboru Murata
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Editor information

Editors and Affiliations

  1. School of Computing, University of the West of Scotland, PA1 2BE, Paisley, UK

    Colin Fyfe

  2. University of Birmingham, B15 2TT, Birmingham, UK

    Peter Tino

  3. University of Ulster, Coleraine, UK

    Darryl Charles

  4. Universidad de Burgos, Burgos, Spain

    Cesar Garcia-Osorio

  5. School of Electrical and Electronic Engineering, University of Manchester, Sackville Street Building, Sackville Street, M60 1QD, Manchester, UK

    Hujun Yin

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Fujimoto, Y., Murata, N. (2010). A Generalization of Independence in Naive Bayes Model. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2010. IDEAL 2010. Lecture Notes in Computer Science, vol 6283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15381-5_19

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  • DOI: https://doi.org/10.1007/978-3-642-15381-5_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15380-8

  • Online ISBN: 978-3-642-15381-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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