Feature selection in a credit scoring model

Laborda Herrero, Juan; Ryoo, Seyong

Publication:
Feature selection in a credit scoring model

dc.affiliation.dpto	UC3M. Departamento de Economía de la Empresa	es
dc.contributor.author	Laborda Herrero, Juan
dc.contributor.author	Ryoo, Seyong
dc.date.accessioned	2021-10-26T10:50:01Z
dc.date.available	2021-10-26T10:50:01Z
dc.date.issued	2021-04-01
dc.description	This article belongs to the Special Issue Mathematics and Mathematical Physics Applied to Financial Markets.	en
dc.description.abstract	This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed.	en
dc.format.extent	22
dc.identifier.bibliographicCitation	Laborda, J. & Ryoo, S. (2021). Feature Selection in a Credit Scoring Model. Mathematics, 9(7), 746.	en
dc.identifier.doi	https://doi.org/10.3390/math9070746
dc.identifier.issn	2227-7390
dc.identifier.publicationfirstpage	746
dc.identifier.publicationissue	7
dc.identifier.publicationtitle	Mathematics	en
dc.identifier.publicationvolume	9
dc.identifier.uri	https://hdl.handle.net/10016/33490
dc.identifier.uxxi	AR/0000028417
dc.language.iso	eng
dc.publisher	MDPI
dc.rights	© 2021 by the authors.	en
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Economía	es
dc.subject.other	Classification algorithms	en
dc.subject.other	Credit scoring	en
dc.subject.other	Feature selection methods	en
dc.subject.other	Machine learning	en
dc.subject.other	Operational research in banking	en
dc.title	Feature selection in a credit scoring model	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication