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Global and Local Rejection Option in Multi–classification Task

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Artificial Neural Networks and Machine Learning – ICANN 2014 (ICANN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8681))

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Abstract

This work presents two rejection options. The global rejection option separates the foreign observations – not defined in the classification task – from the normal observations. The local rejection option works after the classification process and separates observations individually for each class. We present implementation of both methods for binary classifiers grouped in a graph structure (tree or directed acyclic graph). Next, we prove that the quality of rejection is identical for both options and depends only on the quality of binary classifiers. The methods are compared on the handwritten digits recognition task. The local rejection option works better for the most part.

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Authors and Affiliations

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

    Marcin Luckner

Authors
  1. Marcin Luckner
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Editor information

Editors and Affiliations

  1. Department of Informatics, University of Hamburg, Vogt-Kölln-Straße 30, 22527, Hamburg, Germany

    Stefan Wermter , Cornelius Weber  & Sven Magg ,  & 

  2. Department of Informatics, Nicolaus Compernicus University, ul. Grudziądzka 5, 87-100, Torun, Poland

    Włodzisław Duch

  3. Department of Modern Languages, University of Helsinki, P.O. Box 24, 00014, Helsinki, Finland

    Timo Honkela

  4. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl. 25A, 1113, Sofia, Bulgaria

    Petia Koprinkova-Hristova

  5. Institute of Neural Information Processing, University of Ulm, 89069, Oberer Eselsberg, Ulm, Germany

    Günther Palm

  6. Department of Information Systems, Quartier UNIL-Dorigny, Bâtiment Internef, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

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© 2014 Springer International Publishing Switzerland

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Luckner, M. (2014). Global and Local Rejection Option in Multi–classification Task. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_61

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  • DOI: https://doi.org/10.1007/978-3-319-11179-7_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11178-0

  • Online ISBN: 978-3-319-11179-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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