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

  • K. Sirlantzis
  • M. C. Fairhurst
  • M. S. Hoque
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)

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.

Keywords

Genetic Algorithm Recognition Rate Character Recognition Document Image Combination Scheme 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • K. Sirlantzis
    • 1
  • M. C. Fairhurst
    • 1
  • M. S. Hoque
    • 1
  1. 1.Department of ElectronicsUniversity of KentCanterburyUK

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