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A Hybrid Distance-Based and Naive Bayes Online Classifier

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Computational Collective Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9330))

Abstract

The paper combines distance-based weak classifiers constructed using kernel fuzzy clustering technique with the naive Bayes algorithm. Resulting hybrid online ensemble is validated through computational experiment involving a number of datasets often used for testing data streams mining algorithms.

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Author information

Authors and Affiliations

  1. Institute of Informatics, Gdańsk University, Wita Stwosza 57, 80-952, Gdańsk, Poland

    Joanna Jȩdrzejowicz

  2. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Piotr Jȩdrzejowicz

Authors
  1. Joanna Jȩdrzejowicz
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  2. Piotr Jȩdrzejowicz
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Corresponding author

Correspondence to Joanna Jȩdrzejowicz .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Jȩdrzejowicz, J., Jȩdrzejowicz, P. (2015). A Hybrid Distance-Based and Naive Bayes Online Classifier. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-24306-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24305-4

  • Online ISBN: 978-3-319-24306-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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