Neural-based learning classifier systems
UCS is a s (u) under bar pervised learning (c) under bar lassifier (s) under bar ystem that was introduced in 2003 for classification in data mining tasks. The representation of a rule in UCS as a univariate classification rule is straightforward for a human to understand. However, the system may require a large number of rules to cover the input space. Artificial neural networks ( NNs), on the other hand, normally provide a more compact representation. However, it is not a straightforward task to understand the network. In this paper, we propose a novel way to incorporate NNs into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial NN as the classifier's action, we obtain a more compact population size, better generalization, and the same or better accuracy while maintaining a reasonable level of expressiveness. We also apply negative correlation learning ( NCL) during the training of the resultant NN ensemble. NCL is shown to improve the generalization of the ensemble.
classification data mining representations evolutionary computing and genetic algorithms neural nets rule-based processing