期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 3028-3033出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.01.018
关键词
Bankruptcy prediction; Credit scoring; Ensemble of classifiers
In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method Random Subspace outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of Levenberg-Marquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit. (c) 2008 Elsevier Ltd. All rights reserved.
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