Abstract
We present a probabilistic formulation of UCS (a sUpervised Classifier System). UCS
is shown to be a special case of mixture of experts where the experts
are learned independently and later combined during prediction. In this
work, we develop the links between the constituent components of UCS
and a mixture of experts, thus lending UCS a strong analytical
background. We find during our analysis that mixture of experts is a
more generic formulation of UCS and possesses more generalization
capability and flexibility than UCS, which is also verified using
empirical evaluations. This is the first time that a simple
probabilistic model has been proposed for UCS and we believe that this
work will form a useful tool to analyse Learning Classifier Systems
and gain useful insights into their working.
Translated title of the contribution | Modeling UCS as a Mixture of Experts |
---|---|
Original language | English |
Pages (from-to) | 1187-1194 |
Number of pages | 8 |
Journal | Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO'09) |
DOIs | |
Publication status | Published - 2009 |
Bibliographical note
ISBN: 9781605583259Publisher: ACM
Name and Venue of Conference: Proceedings of the 2009 Genetic and Evolutionary Computation Conference (GECCO'09)
Other identifier: 2001012