Prediction using Pittsburgh learning classifier systems: APCS use case

Authors:
Mathias Peroumalnaik;Gilles Énée
Affiliations:
Université des Antilles et de la Guyane, Pointe-à-pitre, Guadeloupe;Université des Antilles et de la Guyane, Pointe-à-pitre, Guadeloupe
Venue:
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Year:
2010

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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.