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Using Consensus Ensembles to Identify Suspect Data

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3214))

Abstract

In a consensus ensemble all members must agree before they classify a data point. But even when they all agree some data is still misclassified. In this paper we look closely at consistently misclassified data to investigate whether some of it may be outliers or may have been mislabelled.

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Author information

Authors and Affiliations

  1. School of Information Sciences and Engineering, University of Canberra, ACT, 2601, Australia

    David Clark

Authors
  1. David Clark
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Clark, D. (2004). Using Consensus Ensembles to Identify Suspect Data. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_63

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  • DOI: https://doi.org/10.1007/978-3-540-30133-2_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23206-3

  • Online ISBN: 978-3-540-30133-2

  • eBook Packages: Springer Book Archive

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