Survey and critique of techniques for extracting rules from trained artificial neural networks |
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Authors: | Robert Andrews Joachim Diederich Alan B Tickle |
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Affiliation: | Neurocomputing Research Centre, Queensland University of Technology, Box 2434 GPO, Brisbane 4001, Queensland, Australia |
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Abstract: | It is becoming increasingly apparent that, without some form of explanation capability, the full potential of trained artificial neural networks (ANNs) may not be realised. This survey gives an overview of techniques developed to redress this situation. Specifically, the survey focuses on mechanisms, procedures, and algorithms designed to insert knowledge into ANNs (knowledge initialisation), extract rules from trained ANNs (rule extraction), and utilise ANNs to refine existing rule bases (rule refinement). The survey also introduces a new taxonomy for classifying the various techniques, discusses their modus operandi, and delineates criteria for evaluating their efficacy. |
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Keywords: | fuzzy neural networks rule extraction rule refinement knowledge insertion inferencing |
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