Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
478580 | European Journal of Operational Research | 2011 | 11 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Steven Finlay,