Abstract
Much effort has been spent during recent years to develop techniques for rule extraction from opaque models, typically trained neural networks. A rule extraction technique could use different strategies for the extraction phase, either a local or a global strategy. The main contribution of this paper is the suggestion of a novel rule extraction method, called Cluster and See Through (CaST), based on the global strategy. CaST uses parts of the well-known RX algorithm, which is based on the local strategy, but in a slightly modified way. The novel method is evaluated against RX and is shown to get as good as or better results on all problems evaluated, with much more compact rules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Craven, M., Shavlik, J.: Using neural networks for data mining. Future Genertion Computer Systems 13, 211–229 (1997)
Lu, H., Setiono, R., Liu, H.: Effective Data Mining Using Neural Networks. IEEE Trans. Knowl. Data Eng. 8(6), 957–961 (1996)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
Johansson, U., König, R., Niklasson, L.: Rule Extraction from Trained Neural Networks Using Genetic Programming. In: 13th International Conference on Artificial Neural Networks (ICANN), Istanbul, Turkey, pp. 13–16 (2003)
Tickle, A., Golea, M., Hayward, R., Diederich, J.: The Truth Is in There: Current Issues in Extracting Rules from Feedfoward Neural Networks. In: Proceedings of the International Conference on Neural Networks, vol. 4, pp. 2530–2534 (1997)
Tickle, A., Andrews, R., Diederich, J.: A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8, 373–389 (1995)
Craven, M.: Extracting comprehensible models from trained neural networks. Ph.D. Thesis, University of Wisconsin, Madison (1996)
Hayashi, Y., Setiono, R., Yoshida, K.: A comparison between two nerual network rule extraction techniques for the diagnosis of the hepatobiliary disorders. Artificial Intelligence in Medicine 20, 205–216 (2000)
Setiono, R., Liu, H.: NeuroLinear: From neural networks to oblique decision rules. Neurocomputing 17, 1–24 (1997)
Lu, H., Setiono, R., Liu, H.: NeuroRule: A Connectionist Approach to Data Mining. In: Proceedings of the 21st VLDB Conference, Zürich, Switzerland (1995)
Craven, M.: Extracting Comprehensible Models form Trained Neural Networks. PhD Thesis, University of Wisconsin, Madison (1996)
Johansson, U.: Rule Extraction. the Key to Accurate and Comprehensible Data Mining Models. Licentiate Thesis, University of Linköping (2004)
Saito, K., Nakano, R.: Medical diagnostic expert system based on PDP model. In: Proceedings of the IEEE International Conference on Neural Networks, San Diego, CA, pp. 255–262. IEEE Press, Los Alamitos (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Löfström, T., Johansson, U., Niklasson, L. (2004). Rule Extraction by Seeing Through the Model. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_85
Download citation
DOI: https://doi.org/10.1007/978-3-540-30499-9_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
eBook Packages: Springer Book Archive