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Neural-based learning classifier systems
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DOI：10.1109/TKDE.2007.190671.png)
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Abstract
En 中文
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.
Keywords:
representations
evolutionary computing and genetic algorithms
neural nets
rule-based processing
data mining
classification
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10.4
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6.8K
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3.2W
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