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Instance sampling in credit scoring: An empirical study of sample size and balancing

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Cited by:

  1. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
  2. José Willer Prado & Valderí Castro Alcântara & Francisval Melo Carvalho & Kelly Carvalho Vieira & Luiz Kennedy Cruz Machado & Dany Flávio Tonelli, 2016. "Multivariate analysis of credit risk and bankruptcy research data: a bibliometric study involving different knowledge fields (1968–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1007-1029, March.
  3. Nehrebecka Natalia, 2018. "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 22(2), pages 54-73, June.
  4. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
  5. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
  6. Natalia Nehrebecka, 2016. "Approach to the assessment of credit risk for non-financial corporations. Evidence from Poland," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Combining micro and macro data for financial stability analysis, volume 41, Bank for International Settlements.
  7. Liu, Wanan & Fan, Hong & Xia, Meng, 2023. "Tree-based heterogeneous cascade ensemble model for credit scoring," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1593-1614.
  8. Marian Nehrebecki, 2023. "Zombification in Poland in particular during COVID-19 pandemic and low interest rates," Bank i Kredyt, Narodowy Bank Polski, vol. 54(2), pages 153-190.
  9. Yang Liu & Fei Huang & Lili Ma & Qingguo Zeng & Jiale Shi, 2024. "Credit scoring prediction leveraging interpretable ensemble learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 286-308, March.
  10. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
  11. Murphy, Sinnott & Sowell, Fallaw & Apt, Jay, 2019. "A time-dependent model of generator failures and recoveries captures correlated events and quantifies temperature dependence," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  12. Antonio Blanco-Oliver & Ana Irimia-Dieguez & María Oliver-Alfonso & Nicholas Wilson, 2015. "Systemic Sovereign Risk and Asset Prices: Evidence from the CDS Market, Stressed European Economies and Nonlinear Causality Tests," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 65(2), pages 144-166, April.
  13. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
  14. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Luz López-Palacios, 2015. "Determinants of Default in P2P Lending," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
  15. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
  16. Mohammad Shamsu Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib & Kunpeng Yuan, 2022. "Modeling credit risk with a multi‐stage hybrid model: An alternative statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1386-1415, November.
  17. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
  18. Mandana Rezaeiahari & Clare C Brown & Mir M Ali & Jyotishka Datta & J Mick Tilford, 2021. "Understanding racial disparities in severe maternal morbidity using Bayesian network analysis," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-18, October.
  19. Rasa Kanapickiene & Renatas Spicas, 2019. "Credit Risk Assessment Model for Small and Micro-Enterprises: The Case of Lithuania," Risks, MDPI, vol. 7(2), pages 1-23, June.
  20. De Novellis, G. & Musile Tanzi, P. & Stanghellini, E., 2024. "Covenant-lite agreement and credit risk: A key relationship in the leveraged loan market," Research in International Business and Finance, Elsevier, vol. 70(PB).
  21. Gero Szepannek, 2022. "An Overview on the Landscape of R Packages for Open Source Scorecard Modelling," Risks, MDPI, vol. 10(3), pages 1-33, March.
  22. Ruize Gao & Shaoze Cui & Yu Wang & Wei Xu, 2025. "Predicting financial distress in high-dimensional imbalanced datasets: a multi-heterogeneous self-paced ensemble learning framework," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
  23. Maldonado, Sebastián & Pérez, Juan & Bravo, Cristián, 2017. "Cost-based feature selection for Support Vector Machines: An application in credit scoring," European Journal of Operational Research, Elsevier, vol. 261(2), pages 656-665.
  24. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
  25. Maisa Cardoso Aniceto & Flavio Barboza & Herbert Kimura, 2020. "Machine learning predictivity applied to consumer creditworthiness," Future Business Journal, Springer, vol. 6(1), pages 1-14, December.
  26. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.
  27. Yinghua Song & Minzhe Jiang & Shixuan Li & Shengzhe Zhao, 2024. "Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 593-614, April.
  28. Xiao, Jin & Zhong, Yu & Jia, Yanlin & Wang, Yadong & Li, Ruoyi & Jiang, Xiaoyi & Wang, Shouyang, 2024. "A novel deep ensemble model for imbalanced credit scoring in internet finance," International Journal of Forecasting, Elsevier, vol. 40(1), pages 348-372.
  29. Xia Li & Hanghang Zheng & Kunpeng Tao & Mao Mao, 2025. "Implementation of an Asymmetric Adjusted Activation Function for Class Imbalance Credit Scoring," Papers 2501.12285, arXiv.org.
  30. Hong Wang & Qingsong Xu & Lifeng Zhou, 2015. "Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-20, February.
  31. Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei, 2022. "Sequential optimization three-way decision model with information gain for credit default risk evaluation," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1116-1128.
  32. Xia Li & Hanghang Zheng & Xiao Chen & Hong Liu & Mao Mao, 2025. "Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring," Papers 2501.10677, arXiv.org, revised Jan 2025.
  33. Ekaterina V. Orlova, 2021. "Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods," Mathematics, MDPI, vol. 9(15), pages 1-28, August.
  34. Dagmar Camska & Jiri Klecka, 2020. "Comparison of Prediction Models Applied in Economic Recession and Expansion," JRFM, MDPI, vol. 13(3), pages 1-16, March.
  35. Mohammad Siami & Mohammad Reza Gholamian & Javad Basiri, 2014. "An application of locally linear model tree algorithm with combination of feature selection in credit scoring," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2213-2222, October.
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