A classification spline machine for building a credit scorecard
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Koo, Ja-Yong | - |
dc.contributor.author | Park, Changyi | - |
dc.contributor.author | Jhun, Myoungshic | - |
dc.date.accessioned | 2021-09-09T01:10:54Z | - |
dc.date.available | 2021-09-09T01:10:54Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2009 | - |
dc.identifier.issn | 0094-9655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/122149 | - |
dc.description.abstract | In constructing a scorecard, we partition each characteristic variable into a few attributes and assign weights to those attributes. For the task, a simulated annealing algorithm has been proposed. A drawback of simulated annealing is that the number of cutpoints separating each characteristic variable into attributes is required as an input. We introduce a scoring method, called a classification spline machine (CSM), which determines cutpoints automatically via a stepwise basis selection. In this paper, we compare performances of CSM and simulated annealing on simulated datasets. The results indicate that the CSM can be useful in the construction of scorecards. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.subject | REGRESSION | - |
dc.title | A classification spline machine for building a credit scorecard | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Koo, Ja-Yong | - |
dc.contributor.affiliatedAuthor | Jhun, Myoungshic | - |
dc.identifier.doi | 10.1080/00949650701859577 | - |
dc.identifier.scopusid | 2-s2.0-78650561721 | - |
dc.identifier.wosid | 000265453400004 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.79, no.5, pp.681 - 689 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.title | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.volume | 79 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 681 | - |
dc.citation.endPage | 689 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordAuthor | cutpoint | - |
dc.subject.keywordAuthor | logistic regression | - |
dc.subject.keywordAuthor | simulated annealing | - |
dc.subject.keywordAuthor | spline basis | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.