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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© 1991 Springer-Verlag Berlin Heidelberg
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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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