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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
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    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.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 192 (2009)
    Issue (Month): 1 (January)
    Pages: 326-332

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    Handle: RePEc:eee:ejores:v:192:y:2009:i:1:p:326-332

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    Keywords: Knowledge discovery Neural networks Rule extraction Credit screening;


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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. 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.
    2. 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.


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