Narayanan Unny Edakunni
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.
- E. Bernado-Mansilla and J. M. Garrell-Guiu. Accuracy-based learning classifier systems : Models, analysis and applications to classification tasks. Evolutionary Computation, 11(3):209--238, 2003. Google Scholar
Digital Library
- Gavin Brown, Jeremy Wyatt, and Peter Tino. Managing diversity in regression ensembles. Journal of Machine Learning Research, 6:1621--1650, 2006. Google ScholarDigital Library
- Arthur Dempster, Nan Laird, and Donald Rubin. Likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1):1--38, 1977.Google Scholar
- Jan Drugowitsch. Design and Analysis of Learning Classifier Systems. Springer-Verlag, 2008. Google ScholarDigital Library
- Narayanan U. Edakunni and Tim Kovacs. Probabilistic modeling of UCS : a theoretical study. Technical report, University of Bristol, 2009.Google Scholar
- R. Jacobs, M. I. Jordan, Nowlan. S. J., and G. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3:79--87, 1991. Google ScholarCross Ref
- Tim Kovacs. Genetics-based machine learning. In Grzegorz Rozenberg, Thomas Back, and Joost Kok, editors, Handbook of Natural Computing: Theory, Experiments, and Applications. Springer Verlag, 2009.Google Scholar
- James Marshall, Gavin Brown, and Tim Kovacs. Bayesian estimation of rule accuracy in UCS. In Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, pages 2831--2834, 2007. Google ScholarDigital Library
Index Terms
- Modeling UCS as a mixture of experts
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