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Título : An exploratory analysis of methods for extracting credit risk rules
Autor : Jimbo Santana, Patricia
Villa Monte, Augusto
Rucci, Enzo
Lanzarini, Laura
Bariviera, Aurelio F.
Palabras clave : PUNTUACIÓN CREDITICIA
REGLAS DE CLASIFICACIÓN
CUANTIZACIÓN DEL VECTOR DE APRENDIZAJE
Fecha de publicación : 2016
Editorial : [s.d.t]
Citación : Jimbo Santana, Patricia y otros (2016). An exploratory analysis of methods for extracting credit risk rules. Journal of computer science & technology, 834-841
Resumen : This paper performs a comparative analysis of two kind of methods for extracting credit risk rules. On one hand we have a set of methods based on the combination of an optimization technique initialized with a neural network. On the other hand there are partition algorithms, based on trees. We show results obtain on two real databases. The main findings are that the set of rules obtained by the first set of methods give a set of rules with a reduced cardinality, with an acceptable precision regarding classification. This is a desirable property for financial in stitutions, who want to decide credit approval face to face with customers. Bank employees who daily deal with retail customers can be easily trained for selecting the best customers, by using this kind of solutions.
URI : http://www.dspace.uce.edu.ec/handle/25000/14603
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