Neural-based learning classifier systems

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

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

89 Citations (Scopus)

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 languageEnglish
Pages (from-to)26-39
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume20
Issue number1
Early online date29 Nov 2007
DOIs
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.

Keywords

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

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