QUT Meritorious Project 1995 An Evaluation And Comparison Of Techniques For Extracting And Refining Rules From Artificial Neural Networks
نویسندگان
چکیده
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. The primary purpose of this report is to survey techniques which have been 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. An additional facet of the report is a comparative evaluation of the performance of a set of techniques developed at the NeuroComputing Research Centre at the QUT to extract knowledge from trained ANNs as a set of symbolic rules.
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