Abstract
Sigmoid kernel is widely applied in neural networks for classification tasks. SVM classifier, which is applied with sigmoid kernel, has excellent classification accuracy. However, as sigmoid kernel has complicated structure, it is generally difficult for human expert to interpret and understand how the sigmoid kernel makes its classification decision. As decision rule classifier is understandable to human expert, in this paper, we present our InterSIG algorithm, which mines decision rules from the classification hyper-plane which is constructed by SVM with sigmoid kernel. InterSIG expands sigmoid kernel into its Maclaurin series, and then mines classification rules which make great contribution to classification from the classification hyper-plane. Experiment results show that InterSIG classifier is more understandable to human experts without jeopardizing the accuracy than the original SVM with sigmoid kernel. Furthermore, compared with 3 association classifiers, CMAR, CBA, CPAR and C4.5, a decision tree classifier, InterSIG classifier is very encouraging over the 9 datasets.
This work is supported by Talent Fund of Northwest A&F University (01140402, 01140406) and Young Cadreman Supporting Program of Northwest A&F University (01140301).
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, New York (2000)
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking State-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627–635 (2003)
Lin, H.T., Lin, C.J.: A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical Report, Neural Computation. Department of Computer Science and Information Engineering, National Taiwan,Taipei (2003)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Martens, D., Baesens, B., Van Gestel, T., Vanthienen, J.: Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines. European journal of operational research 183(3), 1466–1476 (2007)
Zhang, Y., Su, H., Jia, T., Chu, J.: Rule extraction from trained support vector machines. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 61–70. Springer, Heidelberg (2005)
Nunez, H., Angulo, C., Catala, A.: Rule-extraction from support vector machines. In: Proc. European Symposium on Artificial Neural Networks, pp. 107–112 (2002)
Fung, G., Sandilya, S., Bharat Rao, R.: Rule extraction from linear support vector machines. In: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 32–40 (2005)
Barakat, N., Diederich, J.: Eclectic rule-extraction from support vector machines. International Journal of Computational Intelligence 2(1) (2005)
Barakat, N., Diederich, J.: Learning-based Rule-Extraction from Support Vector Machines. In: The 14th International Conference on Computer Theory and applications, ICCTA 2004 (2004)
Zhang, Y., Li, Z.H., Tang, Y., Cui, K.B.: DRC-BK: Mining Classification Rules with Help of SVM. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 191–195. Springer, Heidelberg (2004)
Li, W., Han, J., Pei, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class Association Rules. In: IEEE International Conference on Data Ming, pp. 369–376 (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Pro. the 4th International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 80–86 (1998)
Yin, X., Han, J.: CPAR: Classification Based on Predictive Association Rules. In: Proc. 2003 SIAM Int’I Conf. Data Ming (SDM 2003) (2003)
Quinlan, J.R.: C4.5:Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Mitchell, T.M.: Machine Learning. McGraw-Hill Press, New York (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Q., Zhang, Y., Hu, Z. (2008). Extracting Decision Rules from Sigmoid Kernel. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-88192-6_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88191-9
Online ISBN: 978-3-540-88192-6
eBook Packages: Computer ScienceComputer Science (R0)