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Multiple classifier architectures and their application to credit risk assessment

European journal of operational research, 2011-04, Vol.210 (2), p.368-378 [Peer Reviewed Journal]

ISSN: 0377-2217 ;EISSN: 1872-6860 ;DOI: 10.1016/j.ejor.2010.09.029

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  • Title:
    Multiple classifier architectures and their application to credit risk assessment
  • Author: Finlay, Steven
  • Subjects: OR in banking Data mining Classifier combination Classifier ensembles Credit scoring
  • Is Part Of: European journal of operational research, 2011-04, Vol.210 (2), p.368-378
  • Description: 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.
  • Publisher: Elsevier
  • Language: English
  • Identifier: ISSN: 0377-2217
    EISSN: 1872-6860
    DOI: 10.1016/j.ejor.2010.09.029
  • Source: RePEc
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