Generating predicate rules from neural networks
Nayak, Richi (2005) Generating predicate rules from neural networks. In Sixth International Conference on Intelligent Data Engineering and Automated Learning, July 6-8, 2005, Brisbane, Australia.
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Abstract
Artificial neural networks (ANN) have demonstrated good predictive performance in a wide variety of practical problems. However, due to poor comprehensibility of the learned ANN, and the inability to represent explanation structures, ANNs are not considered sufficient for the general representation of knowledge. This paper details a methodology that represents the knowledge of a trained network in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner.
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| ID Code: | 1471 |
|---|---|
| Item Type: | Conference Paper |
| Keywords: | neural networks, data mining, rule extraction |
| DOI: | 10.1007/11508069_31 |
| ISBN: | 9783-540269724 |
| ISSN: | 1611-3349 |
| Subjects: | Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) |
| Divisions: | Past > QUT Faculties & Divisions > Faculty of Science and Technology |
| Copyright Owner: | Copyright 2005 Springer |
| Copyright Statement: | This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via SpringerLink. http://www.springer.de/comp/lncs/ Lecture Notes in Computer Science |
| Deposited On: | 06 Jun 2005 |
| Last Modified: | 09 Jun 2010 22:25 |
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