Predicting bad credit risk: An evolutionary approach
SE Bedingfield, KA Smith, O Kaynak (ed.), E Alpaydin (ed.), E Oja (ed.), L Xu (ed.)
ARTIFICIAL NEURAL NETWORKS AND NEURAL INFORMATION PROCESSING - ICAN/ICONIP 2003 | SPRINGER-VERLAG BERLIN | Published : 2003
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. © Springer-Verlag Berlin Heidelberg 2003.