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Decision Making Beyond Pattern Recognition: Classification or Rejection

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Intelligent Decision Technologies 2017 (IDT 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 72))

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Abstract

In the paper we discuss the issue of contaminated data sets that contain improper patterns apart from proper ones. To distinguish between those two kinds of patterns we use terms: native (proper) patterns and foreign (garbage) patterns. To deal with contaminated datasets we propose to build decision mechanism based on a collection of classification and regression models that together classify native patterns and reject foreign. The developed approach is empirically evaluated based on a set of handwritten digits as native patterns and handwritten letters playing role of foreign patterns.

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Acknowledgments

The research is partially supported by the National Science Center, grant No 2012/07/B/ST6/01501, decision no DEC-2012/07/B/ST6/01501.

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Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662, Warsaw, Poland

    Wladyslaw Homenda, Agnieszka Jastrzebska & Piotr Waszkiewicz

  2. Faculty of Economics and Informatics in Vilnius, University of Bialystok, Kalvariju G. 135, LT-08221, Vilnius, Lithuania

    Wladyslaw Homenda

Authors
  1. Wladyslaw Homenda
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  2. Agnieszka Jastrzebska
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  3. Piotr Waszkiewicz
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Corresponding author

Correspondence to Wladyslaw Homenda .

Editor information

Editors and Affiliations

  1. Maritime University , Gdynia, Poland

    Ireneusz Czarnowski

  2. Bournemouth University and KES International, Poole, Dorset, United Kingdom

    Robert J. Howlett

  3. University of Canberra, Canberra, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Homenda, W., Jastrzebska, A., Waszkiewicz, P. (2018). Decision Making Beyond Pattern Recognition: Classification or Rejection. In: Czarnowski, I., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2017. IDT 2017. Smart Innovation, Systems and Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-319-59421-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-59421-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59420-0

  • Online ISBN: 978-3-319-59421-7

  • eBook Packages: EngineeringEngineering (R0)

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