Harris, T. (2015) 'Credit scoring using the clustered support vector machine.', Expert systems with applications., 42 (2). pp. 741-750.
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.
|Keywords:||Credit risk, Credit scoring, Clustered support vector machine, Support vector machine.|
|Full text:||(AM) Accepted Manuscript|
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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|Publisher Web site:||http://dx.doi.org/10.1016/j.eswa.2014.08.029|
|Publisher statement:||© 2015 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Record Created:||28 Sep 2015 10:50|
|Last Modified:||28 Sep 2015 13:03|
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