Abstract
This paper proposes a hybrid method to extract rules from the trained Group Method of Data Handling (GMDH) neural network using Decision Tree (DT). The outputs predicted by the GMDH for the training set along with the input variables are fed to the DT for extracting the rules. The effectiveness of the proposed hybrid is evaluated on four benchmark datasets namely Iris, Wine, US Congressional, New Thyroid and one small scale data mining dataset churn prediction using 10-fold cross-validation. One important conclusion from the study is that we obtained statistically significant accuracies at 1% level in the case of churn prediction and IRIS datasets. Further, in the present study, we noticed that the rule base size of proposed hybrid is less in churn prediction and IRIS datasets when compared to that of the DT and equal in the case of remaining datasets.
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
Ravi, V., Arul Shalom, S.A., Manickavel, A.: Sputter Process Variables Prediction via Data Mining. In: proceeding of the 2004 IEEE conference on cybernetics and intelligent systems, Singapore (2004)
Anilkumar, D., Ravi, V.: Predicting credit card customer churn in banks using data mining. International Journal for Data Analysis, Techniques and Strategies 1(1), 4–28 (2008)
Andrews, R., Diederich, J., Tickle, A.B.: A survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge Based Systems 8(6), 373–389 (1996)
Senator, T., Goldberg, H.G., Wooton, J., Cottini, M.A., Umarkhan, A.F., Klinger, C.D., Llamas, W.M., Marrone, M.P., Wong, R.W.H.: The financial crimes enforcement network AI Systems (FAIS): Identifying potential Money Laundering from reports of large cash transaction. AI Magazine 16(4), 21–39 (1995)
Chu, B.H., Tsai, M.-S., Ho, C.-S.: Toward a hybrid data mining model for customer retention. Knowledge-Based Systems 20(8), 703–718 (2007)
Gallant, S.: Connectionist expert systems. Communications of the ACM 31(2), 152–169 (1998)
Kahramanli, H., Allahverdi, N.: Extracting rules for classification problems: AIS based approach. Expert Systems with Application 36, 10494–10602 (2009)
Fu, L.M.: Rule generation from neural networks. IEEE Transaction on Systems, Man and Cybernetics 24(8), 1114–1124 (1994)
Towlell, G.G., Shavlik, J.W.: The extraction of refined rules from knowledge based neural networks. Machine Learning 13(1), 71–101 (1993)
Arbatli, A.D., Akin, H.L.: Rule extraction from trained neural networks using genetic algorithms. Nonlinear Analysis, Theory, Methods and Applications 30(3), 1639–1648 (1997)
Fan, Y., James Li, C.: Diagnostic rule extraction from trained feed forward neural network. Mechanical Systems and Signal Processing 16(6), 107–1081 (2002)
Krishnan, R., Sivakumar, G., Bhattacharya, P.: A Search Technique for rule extraction from trained neural networks. Pattern Recognition Letters 20, 273–280 (1999)
McGarry, K.H., Tait, J., Wermter, S., MacIntyre, J.: Rule extraction from radial basis function networks. In: International conference on Artificial Neural Networks, Edinburgh (1999)
Sato, M., Tsukimoto, H.: Rule extraction from neural networks via Decision Tree Induction, pp. 1870–1875. IEEE, Los Alamitos (2001)
Fujimoto, K., Nakabayashi, S.: Applying GMDH algorithm to extract rules from examples. Systems Analysis Modelling Simulation 43(10), 1311–1319 (2003)
Campos, P.G., Ludermir, T.B.: Literal and ProRulext: Algorithms for rule extraction of ANNs. In: Proceedings of the fifth international conference on Hybrid Intelligent Systems, HIS 2005 (2005)
Aliev, R.A., Aliev, R.R., Guirimov, B., Uyar, K.: Dynamic data mining technique for rules extraction in a process of battery charging. Applied Soft Computing 8, 125–1258 (2008)
Naveen, N., Ravi., V., Raghavendra Rao, C.: Rule extraction from differential evolution trained radial basis function network using genetic algorithms. In: Fifth Annual IEEE conference on Automation Science and Engineering, Bangalore, India, pp. 152–157 (2009)
Farquad, M.A.H., Ravi, V., Bapi, R.S.: Data mining using rules extracted from SVM: an application to churn prediction in banks credit cards. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908. Springer, Heidelberg (2009)
Srinivasan, D.: Energy demand prediction using GMDH networks. Neurocomputing 72(1-3), 625–629 (2008)
Mohanty, R., Ravi, V., Patra, M.R.: Software Reliability Prediction Using Group Method of Data Handling. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908, pp. 344–351. Springer, Heidelberg (2009)
Ivakhnenko, A.G.: The group method of data handling- a rival of the method of stochastic approximation. Soviet Automatic Control 13(3), 43–55 (1966)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo (1992)
Business Intelligence Cup-2004: Organized by the University of Chile (2004), http://www.tis.cl/bicup_04/text-bicup/BICUP/202004/20public/20data.zip
Ravi, V., Kurniawan, H., Thai, P.N.K., Ravikumar, P.: Soft computing system for bank performance prediction. Applied soft computing 8, 305–315 (2008)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 321–357 (2002)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27, 861–874 (2006)
Naveen, N., Ravi, V., Anilkumar, D.: Application of fuzzyARTMAP for churn prediction in bank credit cards. International Journal of Information and Decision Sciences 1(4), 428–444 (2009)
NeuroShell 2.0, http://www.wardsystems.com
Knime 2.0, http://www.knime.org/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Naveen, N., Ravi, V., Raghavendra Rao, C. (2010). Data Mining via Rules Extracted from GMDH: An Application to Predict Churn in Bank Credit Cards. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-15387-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15386-0
Online ISBN: 978-3-642-15387-7
eBook Packages: Computer ScienceComputer Science (R0)