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 0377-2217Full text not available from this repository.
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.
|Journal or Publication Title:||European Journal of Operational Research|
|Uncontrolled Keywords:||OR in banking ; Data mining ; Classifier combination ; Classifier ensembles ; Credit scoring|
|Departments:||Lancaster University Management School > Management Science|
|Deposited On:||11 Jul 2011 19:25|
|Last Modified:||24 Mar 2017 03:45|
Actions (login required)