Predicting bad credit risk: an evolutionary approach

Authors:
Susan E. Bedingfield;Kate A. Smith
Affiliations:
School of Business Systems, Monash University Clayton, Victoria, Australia;School of Business Systems, Monash University Clayton, Victoria, Australia
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

Citing 2
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Abstract

This paper considers classification of binary valued data with unequal misclassification costs. This is a pertinent consideration in many applications of data mining, specifically in the area of credit scoring. An evolutionary algorithm is introduced and employed to generate rule systems for classification. In addition to the misclassification costs various other properties of the classification systems generated by the evolutionary algorithm, such as accuracy and coverage, are considered and discussed.