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Credit scoring using the clustered support vector machine

Harris, T.

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Abstract

This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally.

Citation

Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42(2), 741-750. https://doi.org/10.1016/j.eswa.2014.08.029

Journal Article Type Article
Acceptance Date Aug 23, 2014
Online Publication Date Sep 6, 2014
Publication Date Feb 1, 2015
Deposit Date Sep 24, 2015
Publicly Available Date Mar 29, 2024
Journal Expert Systems with Applications
Print ISSN 0957-4174
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 42
Issue 2
Pages 741-750
DOI https://doi.org/10.1016/j.eswa.2014.08.029
Keywords Credit risk, Credit scoring, Clustered support vector machine, Support vector machine.
Public URL https://durham-repository.worktribe.com/output/1399310

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