Abstract
Although Support Vector Machines (SVMs) have been successfully applied to many problems, they are considered “black box models”. Some methods have been developed to reduce this limitation, among them the FREx_SVM, which extracts fuzzy rules from trained SVMs for multi-class problems. This work deals with an extension to the FREx_SVM method, including a wrapper feature subset selection algorithm for SVMs. The method was evaluated in four benchmark databases. Results show that the proposed extension improves the original FREX_SVM, providing better rule coverage and a lower number of rules, which is a considerable gain in terms of interpretability.
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
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel - based learning methods. Cambridge University Press, Cambridge (2000)
Haykin, S.: Neural Networks - A Comprehensive Foundation. Macmillan College Publishing Company, Basingstoke (1999)
Vapnik, V.N.: Statistical Learning Theory. John Wiley & Sons, Chichester (1998)
Vapnik, V.N., Golowich, S.E., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. In: Advances in Neural Information Processing System, vol. 9, pp. 281–287. Morgan Kaufmann, San Francisco (1997)
Drucker, H.D., Wu, D.H., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. on Neural Networks 10(5), 1048–1054 (1999)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the European Conference on Machine Learning, pp. 137–142. Springer, Heidelberg (1998)
Fu, X., Ong, C.J., Keerthi, S., Hung, G.G., Goh, L.: Extracting the Knowledge Embedded in Support Vector Machines. In: International Joint Conference on Neural Networks (IJCNN 2004), CDROM, Budapest, July 25-29 (2004)
Nunez, H., Angulo, C., Catalá, A.: Rule Extraction From support vectors machines. In: European Symposium on Artificial Neural Networks (ESANN), pp. 107–112 (2002)
Chaves, A., Vellasco, M.M.B.R., Tanscheit, R.: Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification. In: Analysis and Design of Intelligent Systems Using Soft Computing Techniques, ch. II. Advances in Soft Computing, vol. 41, pp. 99–108. Springer, Heidelberg (2007)
Heiler, M., Cremers, D., Schnörr, C.: Efficient Feature Subset Selection for Support Vector Machines. Technical Report 21/2001, Computer Sciences Series, University of Mannheim, Germany (2001)
John, G., Kohavi, R., Pfleger, K.: Irrelevant Features and the Subset Selection Problem. In: Proc. Of the 11th Int. Conf. on Machine Learning, pp. 121–129 (1994)
Moody, J., Utans, J.: Principled architecture selection for neural networks: Application to corporate bond rating prediction. In: Advances in Neural Information Processing Systems, vol. 4, pp. 683–690. Morgan Kaufmann Publishers, San Francisco (1992)
Hsu, C.-W., Lin, C.-J.: A Comparison on Methods for Multi-class Support Vector Machines. IEEE Transaction on Neural Networks 13(2), 415–425 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
da Costa F. Chaves, A., Vellasco, M., Tanscheit, R. (2009). Fuzzy Rules Extraction from Support Vector Machines for Multi-class Classification with Feature Selection. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-03040-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
eBook Packages: Computer ScienceComputer Science (R0)