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Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition

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Multiple Classifier Systems (MCS 2001)

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

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Abstract

We describe a multiple classifier system which incorporates an automatic self-configuration scheme based on genetic algorithms. Our main interest in this paper is focused on exploring the statistical properties of the resulting multi-expert configurations. To this end we initially test the proposed system on a series of tasks of increasing difficulty drawn from the domain of character recognition. We then proceed to investigate the performance of our system not only in comparison to that of its constituent classifiers, but also in comparison to an independent set of individually optimised classifiers. Our results illustrate that significant gains can be obtained by integrating a genetic algorithm based optimisation process into multi-classifier schemes both in the performance enhancement and in the reduction of its volatility, especially as the task domain becomes more complex.

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Author information

Authors and Affiliations

  1. Department of Electronics, University of Kent, Canterbury, Kent, CT2 7NT, UK

    K. Sirlantzis, M. C. Fairhurst & M. S. Hoque

Authors
  1. K. Sirlantzis
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  2. M. C. Fairhurst
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  3. M. S. Hoque
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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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Sirlantzis, K., Fairhurst, M.C., Hoque, M.S. (2001). Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition. 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_10

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  • DOI: https://doi.org/10.1007/3-540-48219-9_10

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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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