Multiple classifier architectures and their application to credit risk assessment

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Multiple classifier architectures and their application to credit risk assessment. / Finlay, Steven M.
In: European Journal of Operational Research, Vol. 210, No. 2, 16.04.2011, p. 368-378.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Finlay, SM 2011, 'Multiple classifier architectures and their application to credit risk assessment', European Journal of Operational Research, vol. 210, no. 2, pp. 368-378. https://doi.org/10.1016/j.ejor.2010.09.029

APA

Finlay, S. M. (2011). Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research, 210(2), 368-378. https://doi.org/10.1016/j.ejor.2010.09.029

Vancouver

Finlay SM. Multiple classifier architectures and their application to credit risk assessment. European Journal of Operational Research. 2011 Apr 16;210(2):368-378. Epub 2010 Sept 29. doi: 10.1016/j.ejor.2010.09.029

Author

Finlay, Steven M. / Multiple classifier architectures and their application to credit risk assessment. In: European Journal of Operational Research. 2011 ; Vol. 210, No. 2. pp. 368-378.

Bibtex

@article{72ec7d0f011a4aa8af592e729a728051,

title = "Multiple classifier architectures and their application to credit risk assessment",

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.",

keywords = "OR in banking, Data mining, Classifier combination, Classifier ensembles, Credit scoring",

author = "Finlay, {Steven M.}",

year = "2011",

month = apr,

day = "16",

doi = "10.1016/j.ejor.2010.09.029",

language = "English",

volume = "210",

pages = "368--378",

journal = "European Journal of Operational Research",

issn = "0377-2217",

publisher = "Elsevier Science B.V.",

number = "2",

}

RIS

TY - JOUR

T1 - Multiple classifier architectures and their application to credit risk assessment

AU - Finlay, Steven M.

PY - 2011/4/16

Y1 - 2011/4/16

N2 - 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.

AB - 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.

KW - OR in banking

KW - Data mining

KW - Classifier combination

KW - Classifier ensembles

KW - Credit scoring

U2 - 10.1016/j.ejor.2010.09.029

DO - 10.1016/j.ejor.2010.09.029

M3 - Journal article

VL - 210

SP - 368

EP - 378

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 2

ER -

Research

Links

Text available via DOI:

Keywords