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

Title:
A note on knowledge discovery using neural networks and its application to credit card screening
Source:
European journal of operational research. 192(1):326-332
Publisher Information:
Amsterdam: Elsevier, 2008.
Publication Year:
2008
Physical Description:
print, 12 ref
Original Material:
INIST-CNRS
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
School of Computing, National University of Singapore, Law Link (Off Kent Ridge Drive) #03-68, Singapore 117590, Singapore
Department of Applied Economic Sciences, K.U. Leuven, Naamsestraat 69, 3000 Leuven, Belgium
Vlerick Leuven Gent Management School, Reep 1, 9000 Gent, Belgium
School of Management. University of Southampton, Southampton S017 IBJ, United Kingdom
ISSN:
0377-2217
Rights:
Copyright 2008 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.20691364
Database:
PASCAL Archive

Weitere Informationen

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.