Full metadata record
DC FieldValueLanguage
dc.contributor.authorChang, Yung-Chiaen_US
dc.contributor.authorChang, Kuei-Huen_US
dc.contributor.authorWu, Guan-Jhihen_US
dc.date.accessioned2019-04-02T05:59:12Z-
dc.date.available2019-04-02T05:59:12Z-
dc.date.issued2018-12-01en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2018.09.029en_US
dc.identifier.urihttp://hdl.handle.net/11536/148460-
dc.description.abstractThe majority of the studies on credit risk assessment models for financial institutions during recent years focus on the improvement of imbalanced data or on the enhancement of classification accuracy with multistage modeling. Whilst multistage modeling and data pre-processing can boost accuracy somewhat, the heterogeneous nature of data may affects the classification accuracy of classifiers. This paper intends to use the classifier, eXtreme gradient boosting tree (XGBoost), to construct a credit risk assessment model for financial institutions. Cluster-based under-sampling is deployed to process imbalanced data. Finally, the area under the receiver operative curve and the accuracy of classifications are the assessment indicators, in the comparison with other frequently used single-stage classifiers such as logistic regression, self-organizing algorithms and support vector machine. The results indicate that the XGBoost classifier used by this paper achieve better results than the other three and can serve as a superior tool for the development of credit risk models for financial institutions. (C) 2018 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjecteXtreme gradient boosting treeen_US
dc.subjectReceiver operative curveen_US
dc.subjectSupport vector machineen_US
dc.subjectCredit risk assessment modelen_US
dc.titleApplication of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutionsen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.asoc.2018.09.029en_US
dc.identifier.journalAPPLIED SOFT COMPUTINGen_US
dc.citation.volume73en_US
dc.citation.spage914en_US
dc.citation.epage920en_US
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000450124900063en_US
dc.citation.woscount0en_US
Appears in Collections:Articles