Abstract
There still exist two key problems required to be solved in the classification rule extraction, i.e. how to select attributes and discretize continuous attributes effectively. The lack of efficient heuristic information is the fundamental reason that affects the performance of currently used approaches. In this paper, a new measure for determining the importance level of the attributes based on the trained SVM is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measure, a new approach for rule extraction from trained SVM and classification problems with continuous attributes is proposed. The performance of the new approach is demonstrated by several computing cases. The experimental results prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated classification problems.
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
Fu, L.M.: Rule generation from neural networks. IEEE Transactions on Systems, Man and Cybernetics 8, 1114–1124 (1994)
Towell, G., Shavlik, J.A.: The extraction of refined rules from knowledge-based neural networks. Machine Learning 1, 71–101 (1993)
Lu, H.J., Setiono, R., Liu, H.: NeuroRule: a connectionist approach to data mining. In: Proceedings of 21th International Conference on Very Large Data Bases, Zurich, Switzerland, pp. 81–106 (1995)
Zhou, Z.H., Jiang, Y., Chen, S.F.: Extracting symbolic rules from trained neural network ensembles. AI Communications 6, 3–15 (2003)
Sestito, S., Dillon, T.: Knowledge acquisition of conjunctive rules using multilayered neural networks. International Journal of Intelligent Systems 7, 779–805 (1993)
Craven, M.W., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: Proceedings of the 11th International Conference on Machine Learning, New Brunswick, NJ, USA, pp. 37–45 (1994)
Maire, F.: Rule-extraction by backpropagation of polyhedra. Neural Networks 12, 717–725 (1999)
Setiono, R., Leow, W.K.: On mapping decision trees and neural networks. Knowledge Based Systems 12, 95–99 (1999)
Battiti, R.A.: Using mutual information for selecting featuring in supervised net neural learning. IEEE Trans on Neural Networks 5, 537–550 (1994)
Bollacker, K.D., Ghosh, J.C.: Mutual information feature extractors for neural classifiers. In: Proceedings of 1996 IEEE international Conference on Neural Networks, Washington, pp. 1528–1533 (1996)
Dash, M., Liu, H., Yao, J.C.: Dimensionality reduction of unsupervised data. In: Proceedings of the 9th International Conference on Tools with Artificial Intelligence, Newport Beach, pp. 532–539 (1997)
Fu, X.J., Wang, L.P.: Data dimensionality reduction with application to simplifying RBF network structure and improving classification performance. IEEE Transactions on Systems, Man and Cybernetics, Part B - Cybernetics 33, 399–409 (2003)
Kamarthi, S.V., Pittner, S.: Accelerating neural network training using weight extrapolation. Neural Networks 12, 1285–1299 (1999)
Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA. (1998), http://www.ics.uci.edu/~meearn/MLRepository.htm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Yang, T., Wang, Z., Fan, Y. (2006). A New Approach to Symbolic Classification Rule Extraction Based on SVM. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_29
Download citation
DOI: https://doi.org/10.1007/978-3-540-36668-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
eBook Packages: Computer ScienceComputer Science (R0)