Instance-dependent misclassification cost-sensitive learning for default prediction
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ribaf.2024.102265
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.References listed on IDEAS
- Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
- Bravo, Cristián & Maldonado, Sebastián & Weber, Richard, 2013. "Granting and managing loans for micro-entrepreneurs: New developments and practical experiences," European Journal of Operational Research, Elsevier, vol. 227(2), pages 358-366.
- Qian Wei & Jianxiong Zhang & Guowei Zhu & Rui Dai & Shichen Zhang, 2020. "Retailer vs. vendor managed inventory with considering stochastic learning effect," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(4), pages 628-646, April.
- De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
- Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
- Campbell R. Harvey & Yan Liu, 2020.
"False (and Missed) Discoveries in Financial Economics,"
Journal of Finance, American Finance Association, vol. 75(5), pages 2503-2553, October.
- Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Papers 2006.04269, arXiv.org.
- Höppner, Sebastiaan & Baesens, Bart & Verbeke, Wouter & Verdonck, Tim, 2022. "Instance-dependent cost-sensitive learning for detecting transfer fraud," European Journal of Operational Research, Elsevier, vol. 297(1), pages 291-300.
- Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
- George Petrides & Darie Moldovan & Lize Coenen & Tias Guns & Wouter Verbeke, 2022. "Cost-sensitive learning for profit-driven credit scoring," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(2), pages 338-350, March.
- Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).
- Hanting Zhou & Wenhe Chen & Changqing Shen & Longsheng Cheng & Min Xia, 2023. "Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN," International Journal of Production Research, Taylor & Francis Journals, vol. 61(23), pages 8252-8264, December.
- P Beling & Z Covaliu & R M Oliver, 2005. "Optimal scoring cutoff policies and efficient frontiers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1016-1029, September.
- H-T Tsai & L C Thomas & H-C Yeh, 2005. "An economic model for credit assessment problems using screening approaches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(7), pages 836-843, July.
- Yang Zong-Chang & Kuang Hong & Xu Ji-sheng & Sun Hong, 2015. "Artificial immune algorithm-based credit evaluation for mobile telephone customers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(9), pages 1533-1541, September.
- Jooken, Jorik & Leyman, Pieter & De Causmaecker, Patrick, 2022. "A new class of hard problem instances for the 0–1 knapsack problem," European Journal of Operational Research, Elsevier, vol. 301(3), pages 841-854.
- Kalpit Patne & Nagesh Shukla & Senevi Kiridena & Manoj Kumar Tiwari, 2018. "Solving closed-loop supply chain problems using game theoretic particle swarm optimisation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5836-5853, September.
- Höppner, Sebastiaan & Stripling, Eugen & Baesens, Bart & Broucke, Seppe vanden & Verdonck, Tim, 2020. "Profit driven decision trees for churn prediction," European Journal of Operational Research, Elsevier, vol. 284(3), pages 920-933.
- Dimitris Bertsimas & Allison O’Hair & Stephen Relyea & John Silberholz, 2016. "An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer," Management Science, INFORMS, vol. 62(5), pages 1511-1531, May.
- Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
- D J Hand & C Whitrow & N M Adams & P Juszczak & D Weston, 2008. "Performance criteria for plastic card fraud detection tools," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(7), pages 956-962, July.
- Verbeke, Wouter & Dejaeger, Karel & Martens, David & Hur, Joon & Baesens, Bart, 2012. "New insights into churn prediction in the telecommunication sector: A profit driven data mining approach," European Journal of Operational Research, Elsevier, vol. 218(1), pages 211-229.
- Gediminas Adomavicius & Yaqiong Wang, 2022. "Improving Reliability Estimation for Individual Numeric Predictions: A Machine Learning Approach," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 503-521, January.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
- Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
- Liu, Zhenkun & De Bock, Koen W. & Zhang, Lifang, 2025. "Explainable profit-driven hotel booking cancellation prediction based on heterogeneous stacking-based ensemble classification," European Journal of Operational Research, Elsevier, vol. 321(1), pages 284-301.
- Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
- Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
- Gómez-Vargas, Nuria & Maldonado, Sebastián & Vairetti, Carla, 2025. "A predict-and-optimize approach to profit-driven churn prevention," European Journal of Operational Research, Elsevier, vol. 324(2), pages 555-566.
- Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
- Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
- Bram Janssens & Matthias Bogaert & Astrid Bagué & Dirk Van den Poel, 2024. "B2Boost: instance-dependent profit-driven modelling of B2B churn," Annals of Operations Research, Springer, vol. 341(1), pages 267-293, October.
- Kazim Topuz & Akhilesh Bajaj & Kristof Coussement & Timothy L. Urban, 2025. "Interpretable machine learning and explainable artificial intelligence," Annals of Operations Research, Springer, vol. 347(2), pages 775-782, April.
- Maarouf, Abdurahman & Feuerriegel, Stefan & Pröllochs, Nicolas, 2025. "A fused large language model for predicting startup success," European Journal of Operational Research, Elsevier, vol. 322(1), pages 198-214.
- Verbeke, Wouter & Olaya, Diego & Guerry, Marie-Anne & Van Belle, Jente, 2023. "To do or not to do? Cost-sensitive causal classification with individual treatment effect estimates," European Journal of Operational Research, Elsevier, vol. 305(2), pages 838-852.
- Liu, Zhenkun & Jiang, Ping & De Bock, Koen W. & Wang, Jianzhou & Zhang, Lifang & Niu, Xinsong, 2024. "Extreme gradient boosting trees with efficient Bayesian optimization for profit-driven customer churn prediction," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
- Tang, Haoxin & Liang, Decui, 2025. "Multi-view reject inference for semi-supervised credit scoring with consistency training and three-way decision," Omega, Elsevier, vol. 133(C).
- Höppner, Sebastiaan & Baesens, Bart & Verbeke, Wouter & Verdonck, Tim, 2022. "Instance-dependent cost-sensitive learning for detecting transfer fraud," European Journal of Operational Research, Elsevier, vol. 297(1), pages 291-300.
- Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Ignatius, Joshua & Cheng, T.C.E. & Marra, Marianna & Guo, Yihan, 2024. "Enhancing e-commerce customer churn management with a profit- and AUC-focused prescriptive analytics approach," Journal of Business Research, Elsevier, vol. 184(C).
- Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
- Abdulrashid, Ismail & Chiang, Wen-Chyuan & Sheu, Jiuh-Biing & Mammadov, Shamkhal, 2025. "An interpretable machine learning framework for enhancing road transportation safety," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 195(C).
- Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
- Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
More about this item
Keywords
; ; ; ; ;JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:69:y:2024:i:c:s0275531924000588. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.