Prediction using Pittsburgh learning classifier systems: APCS use case

Abstract : In this study, we use an adapted version of Pitsburgh-like learning classifier system to perform over classification tasks. The Adapted Pittsburgh Classifier System, enhanced with a new mechanism, allows us to consider the classification problems and their treatment by a given LCS in a different manner. Our aim is to exhibit elements in order to prove that, using the action covering mechanism, this system is able to build an inner map of a given classification learning sample. In the context of this paper, this map is built using an intrisic property of Pittsburgh-like CS: the use of various collections of classifiers amongst a unique population.
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Communication dans un congrès
ACM. Genetic And Evolutionary Computation Conference, Jul 2010, Portland,Oregon, United States. ACM, pp.1901-1908, 2010, <10.1145/1830761.1830823>
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https://hal.archives-ouvertes.fr/hal-00520609
Contributeur : Mathias Peroumalnaïk <>
Soumis le : jeudi 23 septembre 2010 - 18:32:01
Dernière modification le : jeudi 23 septembre 2010 - 18:37:33

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Mathias Peroumalnaïk, Enée Gilles. Prediction using Pittsburgh learning classifier systems: APCS use case. ACM. Genetic And Evolutionary Computation Conference, Jul 2010, Portland,Oregon, United States. ACM, pp.1901-1908, 2010, <10.1145/1830761.1830823>. <hal-00520609>

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