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Título : Obtaining classification rules using lvq +pso: an application to credit risk
Autor : Jimbo Santana, Patricia
Lanzarini, Laura
Villa Monte, Augusto
Fernández Bariviera, Aurelio
Palabras clave : RIESGO DE CRÉDITO
CUANTIZACIÓN DEL VECTOR DE APRENDIZAJE (LVQ)
REGLAS DE CLASIFICACIÓN
OPTIM OPTIMIZACIÓN DE ENJAMBRE DE PARTÍCULAS (PSO)
Fecha de publicación : 2015
Editorial : Suiza: Springer International Publishing
Citación : Jimbo Santana, Patricia y otros (2015). Obtaining classification rules using lvq +pso: an application to credit risk. Scientific Methods for the Treatment of Uncertainty in Social Sciences, 377: 383-391
Resumen : Credit risk management is a key element of financial corporations. One of the main problems that face credit risk officials is to approve or deny a credit petition. The usual decision making process consists in gathering personal and financial information about the borrower. This paper present a new method that is able to generate classifying rules that work no only on numerical attributes, but also on nominal attributes. This method, called LVQ+PSO, combines a competitive neural network with an optimization technique in order to find a reduced set of classifying rules. These rules constitute a predictive model for credit risk approval. Given the reduced quantity of rules, our method is very useful for credit officers aiming to make decisions about granting a credit. Our method was applied to two credit databases that were extensively analyzed by other competing classification methods. We obtain very satisfactory results. Future research lines are exposed
URI : http://www.dspace.uce.edu.ec/handle/25000/14795
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