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A Family of the Online Distance-Based Classifiers

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Intelligent Information and Database Systems (ACIIDS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8398))

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Abstract

In this paper a family of algorithms for the online learning and classification is considered. These algorithms work in rounds, where at each round a new instance is given and the algorithm makes a prediction. After the true class of the instance is revealed, the learning algorithm updates its internal hypothesis. The proposed algorithms are based on fuzzy C-means clustering followed by calculation of distances between cluster centroids and the incoming instance for which the class label is to be predicted. Simple distance-based classifiers thus obtained serve as basic classifiers for the implemented rotation forest kernel. The proposed approach is validated experimentally. Experiment results show that proposed classifiers perform well against competitive approaches.

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Author information

Authors and Affiliations

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

    Joanna Jędrzejowicz

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

    Piotr Jędrzejowicz

Authors
  1. Joanna Jędrzejowicz
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  2. Piotr Jędrzejowicz
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Ngoc Thanh Nguyen  & Bogdan Trawiński  & 

  2. Faculty of Engineering, Department of Computer Engineering, King Mongkut’s Institute of Technology Ladkrabang, 10520, Bangkok, Thailand

    Boonwat Attachoo

  3. Software Engineering Program, King Mongkut’s Institute of Technology Ladkrabang, 10520, Bangkok, Thailand

    Kulwadee Somboonviwat

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© 2014 Springer International Publishing Switzerland

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Jędrzejowicz, J., Jędrzejowicz, P. (2014). A Family of the Online Distance-Based Classifiers. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-05458-2_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05457-5

  • Online ISBN: 978-3-319-05458-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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