Generating predicate rules from neural networks

Nayak, Richi (2005) Generating predicate rules from neural networks. Lecture Notes in Computer Science, 3578, pp. 234-241.

View at publisher

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.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

416 since deposited on 06 Jun 2005
89 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 1471
Item Type: Journal Article
Refereed: Yes
Additional Information: Intelligent Data Engineering and Automated Learning - IDEAL 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005. Proceedings
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: Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2005 Springer-Verlag Berlin Heidelberg
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 00:00
Last Modified: 12 Aug 2013 05:17

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page