Generating rules with predicates, terms and variables from the pruned neural networks
Nayak, Richi (2009) Generating rules with predicates, terms and variables from the pruned neural networks. Neural Networks, 22(4), pp. 405-414.
Artificial neural networks (ANN) have demonstrated good predictive performance in a wide range of applications. They are, however, not considered sufficient for knowledge representation because of their inability to represent the reasoning process succinctly. This paper proposes a novel methodology Gyan that represents the knowledge of a trained network in the form of restricted first-order predicate rules. The empirical results demonstrate that an equivalent symbolic interpretation in the form of rules with predicates, terms and variables can be derived describing the overall behaviour of the trained ANN with improved comprehensibility while maintaining the accuracy and fidelity of the propositional rules.
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|Item Type:||Journal Article|
|Keywords:||Rule Extraction, Connectionist, Neural Networks, Predicate rules|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > School of Information Technology
|Copyright Owner:||Copyright 2009 Elsevier|
|Deposited On:||01 Feb 2010 08:12|
|Last Modified:||01 Mar 2012 00:06|
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