Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
Bayesian estimation of rule accuracy in UCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Adaptive mixtures of local experts
Neural Computation
Design and Analysis of Learning Classifier Systems: A Probabilistic Approach (Studies in Computational Intelligence)
Accuracy exponentiation in UCS and its effect on voting margins
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Online, GA based mixture of experts: a probabilistic model of ucs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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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.