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International Workshop on Multiple Classifier Systems

MCS 2001: Multiple Classifier Systems pp 68–77Cite as

Genetic Programming for Improved Receiver Operating Characteristics

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2096))

Abstract

Genetic programming (GP) can automatically fuse given classifiers of diverse types to produce a combined classifier whose Receiver Operating Characteristics (ROC) are better than [Scott et al.1998b]’s “Maximum Realisable Receiver Operating Characteristics” (MRROC). I.e. better than their convex hull. This is demonstrated on a satellite image processing bench mark using Naive Bayes, Decision Trees (C4.5) and Clementine artificial neural networks.

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Author information

Authors and Affiliations

  1. Computer Science, University College, Gower Street, London, WC1E 6BT, UK

    W. B. Langdon & B. F. Buxton

Authors
  1. W. B. Langdon
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  2. B. F. Buxton
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Editor information

Editors and Affiliations

  1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Josef Kittler

  2. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2001 Springer-Verlag Berlin Heidelberg

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Langdon, W.B., Buxton, B.F. (2001). Genetic Programming for Improved Receiver Operating Characteristics. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_7

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  • DOI: https://doi.org/10.1007/3-540-48219-9_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42284-6

  • Online ISBN: 978-3-540-48219-2

  • eBook Packages: Springer Book Archive

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