Neural-Based Learning Classifier Systems

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 t...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2008-01, Vol.20 (1), p.26-39
Main Authors: Dam, H.H., Abbass, H.A., Lokan, C., Xin Yao
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Artificial neural networks
Computer science
control theory
systems
Computer Society
Data mining
Data processing. List processing. Character string processing
Evolution (biology)
Exact sciences and technology
Genetic algorithms
Humans
Knowledge modeling
Learning
Learning systems
Machine learning
Memory organisation. Data processing
Neural networks
Representations (procedural and rule-based)
Rule-based processing
Software
Studies
Supervised learning
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Summary: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 (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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2007.190671