Multiple classifier architectures and their application to credit risk assessment
Multiple classifier systems combine several individual classifiers to deliver a final classification decision. In this paper the performance of several multiple classifier systems are evaluated in terms of their ability to correctly classify consumers as good or bad credit risks. Empirical results suggest that some multiple classifier systems deliver significantly better performance than the single best classifier, but many do not. Overall, bagging and boosting outperform other multi-classifier systems, and a new boosting algorithm, Error Trimmed Boosting, outperforms bagging and AdaBoost by a significant margin.
Year of publication: |
2011
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Authors: | Finlay, Steven |
Published in: |
European Journal of Operational Research. - Elsevier, ISSN 0377-2217. - Vol. 210.2011, 2, p. 368-378
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Publisher: |
Elsevier |
Keywords: | OR in banking Data mining Classifier combination Classifier ensembles Credit scoring |
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