Two-level classifier ensembles for credit risk assessment
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Other documents of the author: Marqués Marzal, Ana Isabel; García, Vicente; Sánchez Garreta, Josep Salvador
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1016/j.eswa.2012.03.033 |
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Title
Two-level classifier ensembles for credit risk assessmentDate
2012Publisher
ElsevierISSN
0957-4174Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S0957417412005039Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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. [-]
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Expert Systems with Applications, 2012 september, Volume 39, Issue 12Rights
© 2012 Elsevier Ltd. All rights reserved.
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