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A Metaheuristic for Classification of Interval Data in Changing Environments

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Information Technology and Computational Physics (CITCEP 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 462))

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Abstract

The Bayes approach is arguably the classification method most used in unspecialized applications, thanks to its robustness, simplicity, and interpretability. The main problem here is establishing proper probability values. This paper deals with adapting the above method for cases where the classified data is of interval type, with changing environments (evolving data stream, concept drift, nonstationarity). The probability values are estimated using nonparametric methods, thanks to which the procedure becomes independent of characteristics of learning subsets representing particular classes. They can also be supplemented with new, current observations, added while performing the algorithm. The investigated process also removes elements with negligible or even negative impact on accuracy of results, which increases the effectiveness of adaptation in conditions of changing reality. It is possible to differentiate the meanings of particular classes. The method allows any number of them. The particular attributes of data elements may be continuous, categorical, or both.

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Notes

  1. 1.

    Sometimes this procedure performs the function of reflecting reality with mathematics and information technology, which explains why it is occasionally called a model.

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Author information

Authors and Affiliations

  1. AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Kraków, Poland

    Piotr Kulczycki & Piotr A. Kowalski

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Piotr Kulczycki & Piotr A. Kowalski

Authors
  1. Piotr Kulczycki
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  2. Piotr A. Kowalski
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Corresponding author

Correspondence to Piotr Kulczycki .

Editor information

Editors and Affiliations

  1. Faculty of Physics & Applied Comp. Sci, AGH University of Science and Technology Faculty of Physics & Applied Comp. Sci, Kraków, Poland

    Piotr Kulczycki

  2. Faculty of Engineering Sciences, Széchenyi István University Faculty of Engineering Sciences, Győr, Hungary

    László T. Kóczy

  3. Faculty of Civil Engineering, Slovak University of Technology Faculty of Civil Engineering, Bratislava, Slovakia

    Radko Mesiar

  4. Systems Research Institute, Polish Academy of Sciences Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Kulczycki, P., Kowalski, P.A. (2017). A Metaheuristic for Classification of Interval Data in Changing Environments. In: Kulczycki, P., Kóczy, L., Mesiar, R., Kacprzyk, J. (eds) Information Technology and Computational Physics. CITCEP 2016. Advances in Intelligent Systems and Computing, vol 462. Springer, Cham. https://doi.org/10.1007/978-3-319-44260-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-44260-0_2

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  • Print ISBN: 978-3-319-44259-4

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