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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.

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Abstract

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.

Impact and interest:

7 citations in Scopus
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7 citations in Web of Science®

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133 since deposited on 31 Jan 2010
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ID Code: 30070
Item Type: Journal Article
Additional URLs:
Keywords: Rule Extraction, Connectionist, Neural Networks, Predicate rules
DOI: 10.1016/j.neunet.2009.02.001
ISSN: 0893-6080
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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