UCS is a supervised learning classifier system 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, 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 neural networks into UCS. The approach offers a good compromise between compactness, expressiveness, and accuracy. By using a simple artificial neural network 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 neural network ensemble. NCL is shown to improve the generalization of the ensemble. © 2007 IEEE.
|Number of pages
|IEEE Transactions on Knowledge and Data Engineering
|Early online date
|29 Nov 2007
|Published - Jan 2008
Bibliographical noteThe work reported in this paper was funded by the Australian Research Council Linkage (ARC) grant number LP0453657 and Linkage International grant number LX0561255.
- Data mining
- Evolutionary computing and genetic algorithms
- Neural nets
- Rule-based processing