Neural-based learning classifier systems

Hai H. DAM, Hussein A. ABBASS, Chris LOKAN, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

91 Citations (Scopus)


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 languageEnglish
Pages (from-to)26-39
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number1
Early online date29 Nov 2007
Publication statusPublished - Jan 2008
Externally publishedYes

Bibliographical note

The work reported in this paper was funded by the Australian Research Council Linkage (ARC) grant number LP0453657 and Linkage International grant number LX0561255.


  • Classification
  • Data mining
  • Evolutionary computing and genetic algorithms
  • Neural nets
  • Representations
  • Rule-based processing


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