Abstract
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.
Original language | English |
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Pages (from-to) | 26-39 |
Number of pages | 14 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 20 |
Issue number | 1 |
Early online date | 29 Nov 2007 |
DOIs | |
Publication status | Published - Jan 2008 |
Externally published | Yes |
Keywords
- Classification
- Data mining
- Evolutionary computing and genetic algorithms
- Neural nets
- Representations
- Rule-based processing