Skip to main content
SpringerLink
Log in

Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards

  • Conference paper
Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5908))

Abstract

In this work, an eclectic procedure for rule extraction from Support Vector Machine is proposed, where Tree is generated using Naïve Bayes Tree (NBTree) resulting in the SVM+NBTree hybrid. The data set analyzed in this paper is about churn prediction in bank credit cards and is obtained from Business Intelligence Cup 2004. The data set under consideration is highly unbalanced with 93.11% loyal and 6.89% churned customers. Since identifying churner is of paramount importance from business perspective, sensitivity of classification model is more critical. Using the available, original unbalanced data only, we observed that the proposed hybrid SVM+NBTree yielded the best sensitivity compared to other classifiers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Usama, F., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence (1996)

    Google Scholar 

  2. Ravi, V., Arul Shalom, S.A., Manickavel, A.: Sputter Process Variables Prediction via Data Mining. In: Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore (2004)

    Google Scholar 

  3. 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 System (FAIS): Identifying Potential Money Laundering from Reports of Large Cash Transactions. AI Magazine 16(4), 21–39 (1995)

    Google Scholar 

  4. Kumar, D.A., 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)

    Article  Google Scholar 

  5. Bolton, R.N.: A Dynamic model of the Duration of the customer’s relationship with a continuous service provider: The Role of Satisfaction. Marketing Science 17(1), 45–65 (1998)

    Article  MathSciNet  Google Scholar 

  6. Mols, N.P.: The Behavioral consequences of PC banking. International Journal of Bank Marketing 16(5), 195–201 (1998)

    Article  Google Scholar 

  7. Bolton, R.N., Kannan, P.K., Bramlett, M.D.: Implications of Loyalty Program Membership and Service Experiences for Customer Retention and Value. Journal of the Academy of Marketing Science 28(1), 95–108 (2000)

    Article  Google Scholar 

  8. Lejeune, M.A.P.M.: Measuring the impact of data mining on churn management. Electronic Networking Applications and Policy 11(5), 375–387 (2001)

    Article  Google Scholar 

  9. Au, W.-H., Chan, K.C.C., Yao, S.: A Novel Evolutionary Data Mining Algorithm with Applications to Churn Prediction. IEEE Transactions on Evolutionary Computation 7(6), 532–545 (2003)

    Article  Google Scholar 

  10. Larivie‘re, B., Van den Poel, D.: Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications 29(2), 472–484 (2005)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Ron, K.: Scaling Up the Accuracy of Naïve-Bayes Classifiers: a Decisoin-Tree Hybrid. In: Proceedings of KDD 1996, Portland, USA (1996)

    Google Scholar 

  13. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  14. Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. In: Proceedings of the European Conference on Machine Learning. Springer, Heidelberg (1998)

    Google Scholar 

  15. Michel, P., Kaliouby, R.E.: Real time facial expression recognition in video using support vector machines. In: Proceedings of ICMI 2003, Vancouver, British Columbia, Canada, November 5-7, pp. 258–264 (2003)

    Google Scholar 

  16. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.N.: Gene selection for cancer classification using support vector machines. Machine Learning 46(1-3), 389–422 (2002)

    Article  MATH  Google Scholar 

  17. Gallant, S.: Connectionist expert systems. Communications of the ACM 31(2), 152–169 (1988)

    Article  Google Scholar 

  18. Barakath, N.H., Diederich, J.: Eclectic rule-extraction from support vector machines. International journal of Computer Intelligence 2(1), 59–62 (2005)

    Google Scholar 

  19. Nunez, H., Angulo, C., Catata, A.: Rule extraction from support vector machines. In: European Symposium on Artificial Neural Networks Proceedings, pp. 107–112 (2002)

    Google Scholar 

  20. Fung, G., Sandilya, S., Bharat, R.R.: Rule extraction from linear support vector machines. In: Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 32–40. ACM Press, New York (2005)

    Chapter  Google Scholar 

  21. Barakat, N.H., Diederich, J.: Learning-based Rule-Extraction from Support Vector Machines. In: Proceedings of the 14th International Conference on Computer Theory and applications ICCTA 2004, Alexandria, Egypt (2004)

    Google Scholar 

  22. 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)

    Google Scholar 

  23. Barakat, N.H., Bradley, A.P.: Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve. In: The 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong (2006)

    Google Scholar 

  24. Chen, Z., Li, J., Wei, L.: A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue. Artificial Intelligence in Medicine 41, 161–175 (2007)

    Article  Google Scholar 

  25. Chaves, Ad.C.F., Vellasco, M.M.B.R., Tanscheit, R.: Fuzzy rule extraction from support vector machines. In: Fifth International Conference on Hybrid Intelligent Systems, Rio de Janeiro, Brazil, November 06-09 (2005)

    Google Scholar 

  26. Barakat, N.H., Bradley, A.P.: Rule Extraction from Support Vector Machines: A Sequential Covering Approach. IEEE Transactions on Knowledge and Data Engineering 19(6), 729–741 (2007)

    Article  Google Scholar 

  27. Martens, D., Baesens, B., Gestel, T.V.: Decompositional Rule Extraction from Support Vector Machines by Active Learning. IEEE Transactions on Knowledge and Data Engineering 21(2), 178–191 (2009)

    Article  Google Scholar 

  28. Farquad, M.A.H., Ravi, V., Bapi, R.S.: Support Vector Machine based Hybrid Classifiers and Rule Extraction Thereof: Application to Bankruptcy Prediction in Banks. In: Soria, E., Martín, J.D., Magdalena, R., Martínez, M., Serrano, A.J. (eds.) Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques. IGI Global (2008)

    Google Scholar 

  29. Farquad, M.A.H., Ravi, V., Bapi, R.S.: Rule Extraction using Support Vector Machine Based Hybrid Classifier. In: Presented in TENCON-2008, IEEE region 10 Conference, Hyderabad, India, November 19-21 (2008)

    Google Scholar 

  30. Business Intelligence Cup-2004: Organized by the Univeristy of Chile, http://www.tis.cl/bicup_04/text-bicup/BICUP/202004/20public/20data.zip

  31. Naveen, N., Ravi, V., Kumar, D.A.: Application of fuzzyARTMAP for churn prediction in bank credit cards. International Journal of Information and Decision Sciences 1(4), 428–444 (2009)

    Article  Google Scholar 

  32. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001) Software, http://www.csie.ntu.edu.tw/~cjlin/libsvm

  33. Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T.: YALE: Rapid Prototyping for Complex Data Mining Tasks. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2006 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Development and Research in Banking Technology, Castle Hills Road #1, Masab Tank, Hyderabad, 500 057, (AP), India

    M. A. H. Farquad & V. Ravi

  2. Department of Computer & Information Sciences, University of Hyderabad, Hyderabad, 500 046, (AP), India

    M. A. H. Farquad & S. Bapi Raju

Authors
  1. M. A. H. Farquad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Bapi Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Aided Sciences, Kyushu Institute of Technology, 804-8550, Tobata, Kitakyushu, Japan

    Hiroshi Sakai

  2. Department of Pure Mathematics, University of Calcutta, 35 Ballygunge Circular Road, 700019, Kolkata, India

    Mihir Kumar Chakraborty

  3. Information Technology Department, University of Cairo, 5 Ahmed Zewal St. Orman, Giza, Egypt

    Aboul Ella Hassanien

  4. University of Warsaw & Infobright Inc., Poland

    Dominik Ślęzak

  5. University of Electronic Science and Technology of China, Chengdu, China

    William Zhu

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Farquad, M.A.H., Ravi, V., Raju, S.B. (2009). Data Mining Using Rules Extracted from SVM: An Application to Churn Prediction in Bank Credit Cards. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_47

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-10646-0_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-10645-3

  • Online ISBN: 978-3-642-10646-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation