Modeling UCS as a Mixture of Experts

NU Edakunni, TMD Kovacs, Brown Gavin, Marshall James

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

9 Citations (Scopus)


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 contributionModeling UCS as a Mixture of Experts
Original languageEnglish
Title of host publicationGECCO 2009, Montreal
PublisherAssociation for Computing Machinery (ACM)
Pages1187 - 1194
Number of pages8
ISBN (Print)9781605583259
Publication statusPublished - 2009

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 2007 Genetic and Evolutionary Computation Conference (GECCO 2007)


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