Prediction using Pittsburgh learning classifier systems: APCS use case

Mathias Peroumalnaïk (1) , Enée Gilles (1)

1 LAMIA - Laboratoire de Mathématiques Informatique et Applications

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.

Mots clés: Pittsburgh learning classifier systems; apcs; classification; map; segmentation

Domaines: Apprentissage [cs.LG]; Intelligence artificielle [cs.AI]

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