Multiple classifier architectures and their application to credit risk assessment

Steven Finlay

European Journal of Operational Research, 2011, vol. 210, issue 2, 368-378

Abstract: 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.

Keywords: OR; in; banking; Data; mining; Classifier; combination; Classifier; ensembles; Credit; scoring (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (48)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:210:y:2011:i:2:p:368-378

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