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Automatic Design of Multiple Classifier Systems by Unsupervised Learning

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Machine Learning and Data Mining in Pattern Recognition (MLDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1715))

Abstract

In the field of pattern recognition, multiple classifier systems based on the combination of the outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. Previous work clearly showed that multiple classifier systems are effective only if the classifiers forming them make independent errors. This achievement pointed out the fundamental need for methods aimed to design ensembles of “independent” classifiers. However, the most of the recent work focused on the development of combination methods. In this paper, an approach to the automatic design of multiple classifier systems based on unsupervised learning is proposed. Given an initial set of classifiers, such approach is aimed to identify the largest subset of “independent” classifiers. A proof of the optimality of the proposed approach is given. Reported results on the classification of remote sensing images show that this approach allows one to design effective multiple classifier systems.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Cagliari, Italy, Piazza D’Armi, 09123, Cagliari, Italy

    Giorgio Giacinto & Fabio Roli

Authors
  1. Giorgio Giacinto
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  2. Fabio Roli
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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Arno-Nitzsche-Str. 45, D-04277, Leipzig, Germany

    Petra Perner

  2. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guilford, GU2 5XH, UK

    Maria Petrou

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© 1999 Springer-Verlag Berlin Heidelberg

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Giacinto, G., Roli, F. (1999). Automatic Design of Multiple Classifier Systems by Unsupervised Learning. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_11

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  • DOI: https://doi.org/10.1007/3-540-48097-8_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66599-1

  • Online ISBN: 978-3-540-48097-6

  • eBook Packages: Springer Book Archive

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