Abstract
Automatic design of fuzzy rule-based classification systems based on labeled data is considered. It is recognized that both classification performance and interpretability are of major importance and effort is made to keep the resulting rule bases small and comprehensible. An iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from the data and subsequently, feature selection and rule base simplification are applied to reduce the model, and a GA is used for model tuning. An application to the Wine data classification problem is shown.
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
Babuska R. (1998) Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston.
Cios K.J., Pedrycz W., Swiniarski R.W. (1998) Data Mining Methods for Knowledge Discovery. Kluwer Academic Press, Boston.
Corcoran A.L., Sen S. (1994) Using real-valued genetic algorithms to evolve rule sets for classification. In IEEE-CEC, June 27–29, 120–124, Orlando, USA.
Gustafson, D.E., Kessel, W.C. (1979) Fuzzy clustering with a fuzzy covariance matrix, In Proc. IEEE CDC, 761–766, San Diego, USA.
Ishibuchi H., Nakashima T., Murata T. (1999) Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. SMC—B 29, 601–618.
Nauck D., Kruse R. (1999) Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 16, 149–169.
Roubos J.A., Setnes M. (2000) Compact fuzzy models through complexity reduction and evolutionary optimization. In FUZZ-IEEE, 762–767, May 7–10, San Antonio, USA.
Setiono R. (2000) Generating concise and accurate classification rules for breast cancer diagnosis. Artificial Intelligence in Medicine 18, 205–219.
Setnes M., Babuska R. (1999) Fuzzy relational classifier trained by fuzzy clustering, IEEE Trans. SMC—B 29, 619–625
Setnes M., Babuska R., Kaymak U., van Nauta Lemke H.R. (1998) Similarity measures in fuzzy rule base simplification. IEEE Trans. SMC—B 28, 376–386.
Setnes M., Roubos J.A. (1999) Transparent fuzzy modeling using fuzzy clustering and GA’s. In NAFIPS, June 10–12, 198–202, New York, USA.
Setnes M., Roubos J.A. (in press, 2000) GA-fuzzy modeling and classification: complexity and performance. IEEE Trans. FS.
Valente de Oliveira J. (1999) Semantic constraints for membership function optimization. IEEE Trans. FS 19, 128–138.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roubos, H., Setnes, M., Abonyi, J. (2001). Learning Fuzzy Classification Rules from Data. In: John, R., Birkenhead, R. (eds) Developments in Soft Computing. Advances in Soft Computing, vol 9. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1829-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-7908-1829-1_13
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1361-6
Online ISBN: 978-3-7908-1829-1
eBook Packages: Springer Book Archive