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International Symposium on Methodologies for Intelligent Systems

ISMIS 1991: Methodologies for Intelligent Systems pp 286–295Cite as

Using genetic algorithms to improve the performance of classification rules produced by symbolic inductive methods

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 542))

Abstract

In this paper we present a novel way of combining symbolic inductive methods and genetic algorithms (GAs) applied to produce high-performance classification rules. The presented method consists of two phases. In the first one the algorithm induvtively learns a set of classification rules from noisy input examples. In the second phase the worst performing rule is optimized by GAs techniques. Experimental results are presented for twelve classes of noisy data obtained from textured images.

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References

  • Bala, J.W. and Pachowicz, P.W., “Application of Symbolic Machine Learning to the Recognition of Texture Concepts” 7th IEEE Conference on Artificial Intelligence Applications, Miami Beach FL, February 1991.

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  • Vafaie H.and De Jong, K. “Improving the Performance of a Rule Induction System Using Genetic Algorithms”; in preparation.(Center for AI, GMU 1991)

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Author information

Authors and Affiliations

  1. Center for Artificial Intelligence, George Mason University, 4400 University Drive, 22030, Fairfax, VA

    Jerzy Bala, Kenneth DeJong & Peter Pachowicz

Authors
  1. Jerzy Bala
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  2. Kenneth DeJong
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  3. Peter Pachowicz
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Editor information

Z. W. Ras M. Zemankova

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© 1991 Springer-Verlag Berlin Heidelberg

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Cite this paper

Bala, J., DeJong, K., Pachowicz, P. (1991). Using genetic algorithms to improve the performance of classification rules produced by symbolic inductive methods. In: Ras, Z.W., Zemankova, M. (eds) Methodologies for Intelligent Systems. ISMIS 1991. Lecture Notes in Computer Science, vol 542. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54563-8_92

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  • DOI: https://doi.org/10.1007/3-540-54563-8_92

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54563-7

  • Online ISBN: 978-3-540-38466-3

  • eBook Packages: Springer Book Archive

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