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A note on knowledge discovery using neural networks and its application to credit card screening

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  • Setiono, Rudy
  • Baesens, Bart
  • Mues, Christophe

Abstract

We address an important issue in knowledge discovery using neural networks that has been left out in a recent article "Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem" by Sexton et al. [R.S. Sexton, S. McMurtrey, D.J. Cleavenger, Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem, European Journal of Operational Research 168 (2006) 1009-1018]. This important issue is the generation of comprehensible rule sets from trained neural networks. In this note, we present our neural network rule extraction algorithm that is very effective in discovering knowledge embedded in a neural network. This algorithm is particularly appropriate in applications where comprehensibility as well as accuracy are required. For the same data sets used by Sexton et al. our algorithm produces accurate rule sets that are concise and comprehensible, and hence helps validate the claim that neural networks could be viable alternatives to other data mining tools for knowledge discovery.

Suggested Citation

  • Setiono, Rudy & Baesens, Bart & Mues, Christophe, 2009. "A note on knowledge discovery using neural networks and its application to credit card screening," European Journal of Operational Research, Elsevier, vol. 192(1), pages 326-332, January.
    • Handle: RePEc:eee:ejores:v:192:y:2009:i:1:p:326-332
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    References listed on IDEAS

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    1. Sexton, Randall S. & McMurtrey, Shannon & Cleavenger, Dean, 2006. "Knowledge discovery using a neural network simultaneous optimization algorithm on a real world classification problem," European Journal of Operational Research, Elsevier, vol. 168(3), pages 1009-1018, February.
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    Cited by:

    1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    2. Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
    3. Koutanaei, Fatemeh Nemati & Sajedi, Hedieh & Khanbabaei, Mohammad, 2015. "A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 11-23.
    4. Brad S. Trinkle & Amelia A. Baldwin, 2016. "Research Opportunities for Neural Networks: The Case for Credit," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 240-254, July.
    5. Hayashi, Yoichi, 2016. "Application of a rule extraction algorithm family based on the Re-RX algorithm to financial credit risk assessment from a Pareto optimal perspective," Operations Research Perspectives, Elsevier, vol. 3(C), pages 32-42.
    6. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

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