On the use of data filtering techniques for credit risk prediction with instance-based models
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Scholar |
Otros documentos de la autoría: García, Vicente; Sánchez Garreta, Josep Salvador; Marqués Marzal, Ana Isabel
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http://dx.doi.org/10.1016/j.eswa.2012.05.075 |
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Título
On the use of data filtering techniques for credit risk prediction with instance-based modelsFecha de publicación
2012Editor
ElsevierISSN
0957-4174Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://ac.els-cdn.com/S0957417412007919/1-s2.0-S0957417412007919-main.pdf?_tid=d ...Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Many techniques have been proposed for credit risk prediction, from statistical models to artificial intelligence methods. However, very few research efforts have been devoted to deal with the presence of noise and ... [+]
Many techniques have been proposed for credit risk prediction, from statistical models to artificial intelligence methods. However, very few research efforts have been devoted to deal with the presence of noise and outliers in the training set, which may strongly affect the performance of the prediction model. Accordingly, the aim of the present paper is to systematically investigate whether the application of filtering algorithms leads to an increase in accuracy of instance-based classifiers in the context of credit risk assessment. The experimental results with 20 different algorithms and 8 credit databases show that the filtered sets perform significantly better than the non-preprocessed training sets when using the nearest neighbour decision rule. The experiments also allow to identify which techniques are most robust and accurate when confronted with noisy credit data. [-]
Publicado en
Expert Systems with Applications Volume 39, Issue 18, 15 December 2012Derechos de acceso
© 2012 Elsevier Ltd. All rights reserved.
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