Abstract
Fuzzy Rule-Based System (FRB) in the form of human comprehensible IF-THEN rules can be extracted from Support Vector Machine (SVM) which is regarded as a black-boxed system. We first prove that SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS) are equivalent indicating that SVM’s decision can actually be represented by fuzzy IF-THEN rules. We then propose a rule extraction method based on kernel function firing strength and unbounded support vector space expansion. An advantage of our method is the guarantee that the number of final fuzzy IF-THEN rules is equal or less than the number of support vectors in SVM, and it may reveal human comprehensible patterns. We compare our method against SVM using popular benchmark data sets, and the results are comparable.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aeberhard S., Coomans D., de Vel, O.: The Classification Performance of RDA. Tech. Rep., no. 92-01 (1992)
Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389 (1995)
Barakat, N., Diederich, J.: Eclectic Rule-Extraction from Support Vector Machines. International Journal of Computational Intelligence 2(1), 59–62 (2005)
Diederich, J. (ed.): Rule Extraction from Support Vector Machines. SCI, vol. 80, pp. 3–30. Springer, Heidelberg (2008)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annual Eugenics 7(Part II), 179–188 (1936)
Fung, G., Sandilya, S., Rao, R.B.: Rule extraction from linear support vector machines. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 32–40 (2005)
Haberman, S.J.: Generalized Residuals for Log-Linear Models. In: Proceedings of the 9th International Biometrics Conference, Boston, pp. 104–122 (1976)
Huysmans, J., Baesens, B., Vanthienen, J.: ITER: An algorithm for predictive regression rule extraction. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 270–279. Springer, Heidelberg (2006)
Huysmans, J., Setiono, R., Baesens, B., Vanthienen, J.: Minerva: Sequential Covering for Rule Extraction. IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics 38(2), 299–309 (2008)
Jang, J.S.R., Sun, C.T.: Functional equivalence between radial basis function networks and fuzzy inference systems. IEEE Trans. Neural Networks 4, 156–158 (1992)
Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing, pp. 333–342. Prentice Hall International, Englewood Cliffs (1997)
Kolman, E., Margaliot, M.: Are artificial neural networks white boxes? IEEE Trans. Neural Networks 16(4), 844–852 (2005)
Kumar, S.: Neural Networks: A Classroom Approach, International Edition, pp. 273–304. McGraw-Hill, New York (2005)
Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M., Goodenday, L.S.: Knowledge Discovery Approach to Automated Cardiac SPECT Diagnosis. Artificial Intelligence in Medicine 23(2), 149–169 (2001)
Nunez, H., Angulo, C., Catala, A.: Rule Extraction Based on Support and Prototype Vectors. SCI, vol. 80, pp. 109–134. Springer, Heidelberg (2008)
Sigillito, V.J., Wing, S.P., Hutton, L.V., Baker, K.B.: Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Technical Digest 10, 262–266 (1989)
Vapnik, V.N.: Statistical Learning Theory, pp. 375–520. John Wiley & Sons, Chichester (1998)
Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proceedings of the National Academy of Sciences 87, 9193–9196 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pitiranggon, P., Benjathepanun, N., Banditvilai, S., Boonjing, V. (2011). Rule Extraction for Support Vector Machine Using Input Space Expansion. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20042-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-20042-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20041-0
Online ISBN: 978-3-642-20042-7
eBook Packages: Computer ScienceComputer Science (R0)