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Multiple classifier architectures and their application to credit risk assessment

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published
<mark>Journal publication date</mark>16/04/2011
<mark>Journal</mark>European Journal of Operational Research
Issue number2
Volume210
Number of pages11
Pages (from-to)368-378
Publication StatusPublished
Early online date29/09/10
<mark>Original language</mark>English

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.