Multiple classifier architectures and their application to credit risk assessment

Finlay, Steven M. (2011) Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210 (2). pp. 368-378. ISSN 0377-2217

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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.

Item Type:
Journal Article
Journal or Publication Title:
European Journal of Operational Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2611
Subjects:
?? or in bankingdata miningclassifier combinationclassifier ensemblescredit scoringmodelling and simulationmanagement science and operations researchinformation systems and managementdiscipline-based research ??
ID Code:
45057
Deposited By:
Deposited On:
11 Jul 2011 18:25
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 12:04