Adaptive mixtures of local experts
Neural Computation
Design and Analysis of Learning Classifier Systems: A Probabilistic Approach (Studies in Computational Intelligence)
Modeling UCS as a mixture of experts
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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In recent years there have been efforts to develop a probabilistic framework to explain the workings of a Learning Classifier System. This direction of research has met with limited success due to the intractability of complicated heuristic training rules used by the learning classifier systems. In this paper, we derive a learning classifier system from a mixture of experts that is similar to a sUpervised Classifier System (UCS) in terms of its training and prediction routines. We start by framing the learning model as a mixture of experts which uses an Expectation Maximisation (EM) procedure to learn its parameters. The batch updates of the EM is then converted into online updates and finally into a GA based sampled online update thus ending up with a classifier system similar to a sUpervised Classifier System. In this paper, we show the effectiveness of such a system as compared to UCS through a series of comparative studies on test datasets.