Two-level classifier ensembles for credit risk assessment
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http://dx.doi.org/10.1016/j.eswa.2012.03.033 |
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Título
Two-level classifier ensembles for credit risk assessmentFecha de publicación
2012Editor
ElsevierISSN
0957-4174Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://www.sciencedirect.com/science/article/pii/S0957417412005039Versión
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 generally more accurate than single prediction models. The present paper goes one step beyond by introducing composite ensembles that jointly use different strategies for diversity induction. Accordingly, the combination of data resampling algorithms (bagging and AdaBoost) and attribute subset selection methods (random subspace and rotation forest) for the construction of composite ensembles is explored with the aim of improving the prediction performance. The experimental results and statistical tests show that this new two-level classifier ensemble constitutes an appropriate solution for credit scoring problems, performing better than the traditional single ensembles and very significantly better than individual classifiers. [-]
Publicado en
Expert Systems with Applications, 2012 september, Volume 39, Issue 12Derechos de acceso
© 2012 Elsevier Ltd. All rights reserved.
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