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Online Learning Based on Prototypes

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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

The problem addressed in this paper concerns learning form data streams with concept drift. The goal of the paper is to propose a framework for the online learning. It is assumed that classifiers are induced from incoming blocks of prototypes, called data chunks. Eachdata chunk consists of prototypes including also information as to whether the class prediction of these instances was correct or not. When a new data chunk is formed, classifier ensembles formed at an earlier stage are updated. Three online learning algorithms for performing machine learning on data streams based on three different prototype selection approaches to forming data chunks are considered. The proposed approach is validated experimentally and the computational experiment results are discussed.

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Author information

Authors and Affiliations

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

    Ireneusz Czarnowski & Piotr Jędrzejowicz

Authors
  1. Ireneusz Czarnowski
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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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Czarnowski, I., Jędrzejowicz, P. (2014). Online Learning Based on Prototypes. 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_20

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  • DOI: https://doi.org/10.1007/978-3-319-05458-2_20

  • 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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