Abstract
The BRAINNE method is a technique for extracting symbolic production rules, concepts and concept hierarchies from neural networks. Previous work reported on the extraction of conjunctive and disjunctive rules for the case of binary, discrete and continuous features. In this paper we explain three new improvements. The first improvement uses a hybrid two layer network for learning disjunctive rules, where the first layer consists of a network that uses unsupervised learning and the second layer uses supervised learning. The second improvement examines the effect on the generalisation capability of the rules developed by avoiding overtraining of the neural network, and the third development is an improved approach to dealing with continuous features which greatly increases the generalisation capability of the derived rules.
Chapter PDF
Similar content being viewed by others
References
Bloomer, W.F., Dillon, T.S. and Witten, M. (1996) Hybrid BRAINNE: A method for developing symbolic rules from a hybrid neural network. IEEE International Conference on Systems Man and Cybernetics. Beijing China. October. pp. 14–17.
Craven, M.W. and Shavlik, J.W. (1993) Learning symbolic rules using artificial neural networks. Proceedings of the Tenth International Conference on Machine Learning. Amherst, MA. pp. 73–80.
Dillon, T.S., Sestito, S., Witten, M. and Suing, M. (1993) Automated knowledge acquisition using unsupervised learning. Proceedings of the Second IEEE Workshop on Emerging Technology and Factory Automation (EFTA ‘83). Cairns. September. pp. 119–28.
Dillon, T.S., Sestito, S., Witten, M. and Suing, M. (1994) Symbolic knowledge from unsupervised learning. International Symposium on Integrated Knowledge and Neural Heuristics. Florida USA. May. pp. 47–56.
El-Sharkawi, M.A. (1996) Neural networks and its ancillary techniques as applied to power systems. In IEEE Tutorial Course on Artificial Neural Networks with Application to Power Systems. (eds M.A. El-Sharkawi and D. Niebur ).
Fu, L. (1991) Rule learning by searching on adapted nets. Proceedings of the Ninth National Conference on Artificial Intelligence. Anaheim, CA. pp. 590–5.
Gallant, S.I. (1993) Neural Network Learning and Expert Systems. MIT Press, Cambridge, MA.
Kohonen, T. (1990) The self organising map. Proceedings of the IEEE, 78(9) September, 1464–80.
McClelland, J.L. and Rumelhart, D.E. (1988) Explorations in Parallel Distributed Processing: A Handbook of Models, Programs and Exercises. MIT Press, Cambridge Massachusetts. pp. 83–137.
Saito, K. and Nakano, R. (1988) Medical diagnostic expert system based on PDP model. Proceedings of the IEEE International Conference on Neural Networks. San Diego, CA. pp. 255–62.
Sestito, S. and Dillon, T.S. (1989) Using neural networks for the extraction of high level knowledge representation for machine learning. Australian Artificial Intelligence Conference (AI ‘89). Melbourne Australia. pp. 413–28.
Sestito, S. and Dillon, T.S. (1990a) Using sub-symbolic methods for machine learning of high level knowledge representation. Keynote paper. Finnish AI Symposium (SteP-’90). Oulu Finland. pp. 27–49.
Sestito, S. and Dillon, T.S. (1990b) Using multi-layered neural networks for learning symbolic knowledge. Australian Artificial Intelligence Conference (AI ‘80). Perth Australia. November. pp. 249–62.
Sestito, S. and Dillon, T.S. (1990c) Machine learning using single layered and multi-layered neural. IEEE Conference on Tools for AI (TA!-’90). Washington D.C. November. pp. 269–75.
Sestito, S. and Dillon, T.S. (1991a) Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications. Journal of Artificial Intelligence. 1 November, 157–73.
Sestito, S. and Dillon, T.S. (1991b) The use of sub-symbolic methods for the automation of knowledge acquisition for expert systems. Eleventh International Conference on Expert Systems and their Applications (Avignon ‘81). Avignon France. pp. 317–28.
Sestito, S. and Dillon, T.S. (1992) Automated knowledge acquisition of rules with continuously valued attributes. Twelfth International Conference on Expert Systems and their Applications (Avignon ‘82). Avignon France. pp. 645–56.
Sestito, S. and Dillon, T.S. (1993) Knowledge acquisition with neural networks. In Modern Tools for Manufacturing Systems. Elsevier Science Publishers BV, Nederland. pp. 149–76.
Sestito, S. and Dillon, T.S. (1994) Automated Knowledge Acquisition. Prentice Hall, Sydney.
Towell, G. and Shavlik, J. (1993) Extracting refined rules from knowledge-based neural networks. Machine Learning. 13 (1), 71–101.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Dillon, T.S., Hossain, T., Bloomer, W., Witten, M. (1998). Improvements in supervised BRAINNE: A method for symbolic datamining using neural networks. In: Spaccapietra, S., Maryanski, F. (eds) Data Mining and Reverse Engineering. IFIP — The International Federation for Information Processing. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35300-5_4
Download citation
DOI: https://doi.org/10.1007/978-0-387-35300-5_4
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-4910-6
Online ISBN: 978-0-387-35300-5
eBook Packages: Springer Book Archive