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A Framework for Adaptive and Integrated Classification

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Book cover Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

This paper focuses on classification tasks. The goal of the paper is to propose a framework for adaptive and integrated machine classification and to investigate the effect of different adaptation and integration schemes. After having introduced several integration and adaptation schemes a framework for adaptive and integrated classification in the form of the software shell is proposed. The shell allows for integrating data pre-processing with data mining stages using population-based and A-Team techniques. The approach was validated experimentally. Experiment results have shown that integrated and adaptive classification outperforms traditional approaches.

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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 Jȩdrzejowicz

Authors
  1. Ireneusz Czarnowski
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  2. Piotr Jȩdrzejowicz
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Czarnowski, I., Jȩdrzejowicz, P. (2008). A Framework for Adaptive and Integrated Classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_51

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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