Exploring the behaviour of base classifiers in credit scoring ensembles
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Scholar |
Otros documentos de la autoría: Marqués Marzal, Ana Isabel; García, Vicente; Sánchez Garreta, Josep Salvador
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http://dx.doi.org/10.1016/j.eswa.2012.02.092 |
Metadatos
Título
Exploring the behaviour of base classifiers in credit scoring ensemblesFecha de publicación
2012Editor
ElsevierISSN
0957-4174Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://ac.els-cdn.com/S0957417412003363/1-s2.0-S0957417412003363-main.pdf?_tid=e ...Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been ... [+]
Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been applied to credit scoring problems, demonstrating to be more accurate than single prediction models. However, it is still a question what base classifiers should be employed in each ensemble in order to achieve the highest performance. Accordingly, the present paper evaluates the performance of seven individual prediction techniques when used as members of five different ensemble methods. The ultimate aim of this study is to suggest appropriate classifiers for each ensemble approach in the context of credit scoring. The experimental results and statistical tests show that the C4.5 decision tree constitutes the best solution for most ensemble methods, closely followed by the multilayer perceptron neural network and logistic regression, whereas the nearest neighbour and the naive Bayes classifiers appear to be significantly the worst. [-]
Publicado en
Expert Systems with Applications Volume 39, Issue 11, 1 September 2012Derechos de acceso
© 2012 Elsevier Ltd. All rights reserved.
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