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

Rudy Setiono, Bart Baesens and Christophe Mues

European Journal of Operational Research, 2009, vol. 192, issue 1, 326-332

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.

Keywords: Knowledge; discovery; Neural; networks; Rule; extraction; Credit; screening (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (6)

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