DC Field | Value | Language |
---|---|---|
dc.contributor.author | Son, H. | - |
dc.contributor.author | Hyun, C. | - |
dc.contributor.author | Phan, D. | - |
dc.contributor.author | Hwang, H.J. | - |
dc.date.accessioned | 2019-12-03T12:50:08Z | - |
dc.date.available | 2019-12-03T12:50:08Z | - |
dc.date.created | 2019-08-07 | - |
dc.date.issued | 2019-12 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/100212 | - |
dc.description.abstract | Bankruptcy prediction problem has been intensively studied over the past decades. From traditional statistical models to state of the art machine learning models, various predictive models are developed and applied to various datasets. However, models that use machine learning are not used in the field of business, for two main reasons. First, the prediction accuracy does not far exceed the statistical models and second, the results are not interpretable. In this study, we focused on solving the skewness which is a characteristic of financial data. By solving this problem, we obtained 17% average improvement in AUC over existing models. To address the second shortcoming, we analyze the importance of features identified by the XGBoost model. The interpretation of the model differs among categories of data. Our bankruptcy prediction model has high predictive accuracy with clear explanations and is therefore directly applicable to the industry. (C) 2019 Elsevier Ltd. All rights reserved. | - |
dc.language | English | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.title | Data analytic approach for bankruptcy prediction | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.eswa.2019.07.033 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.138 | - |
dc.identifier.wosid | 000489189900006 | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 138 | - |
dc.contributor.affiliatedAuthor | Phan, D. | - |
dc.contributor.affiliatedAuthor | Hwang, H.J. | - |
dc.identifier.scopusid | 2-s2.0-85069566241 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordPlus | Analytic approach | - |
dc.subject.keywordPlus | Bankruptcy prediction | - |
dc.subject.keywordPlus | Boosting | - |
dc.subject.keywordPlus | Feature importance | - |
dc.subject.keywordPlus | Machine learning models | - |
dc.subject.keywordPlus | Prediction accuracy | - |
dc.subject.keywordPlus | Predictive accuracy | - |
dc.subject.keywordPlus | Preprocessing | - |
dc.subject.keywordPlus | Metadata | - |
dc.subject.keywordPlus | Adaptive boosting | - |
dc.subject.keywordPlus | Data reduction | - |
dc.subject.keywordAuthor | Bankruptcy prediction | - |
dc.subject.keywordAuthor | Boosting | - |
dc.subject.keywordAuthor | Box–Cox transformation | - |
dc.subject.keywordAuthor | Data analysis | - |
dc.subject.keywordAuthor | Feature importance | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Preprocessing | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
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