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Credit risk analysis using boosting methods Cover
By: S. B. Coşkun and  M. Turanli  
Open Access
|Jun 2023

References

  1. Breiman, L., Friedman, J. H., Olshen, R.A., Stone, C.J., 1984. Classification and Regression Trees, Chapman & Hall/CRC.
  2. Burkov, A. 2019. The Hundred Page Machine Learning Book, Andriy Burkov.
  3. Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM
  4. Giussani, A., 2019. Applied Machine Learning with Python, Egea S.p.A. SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
  5. Guo, W., Zhou, Z.Z., 2022. A comparative study of combining tree-based feature selection methods and classifiers in personal loan default prediction. Journal of Forecasting, 41, 1248-1313.
  6. James, G., Witten, D., Hastie, T., Tibshirani, R., 2021. An Introduction to Statistical Learning with Applications in R, 2nd ed., Springer, New York.
  7. Jhaveri, S., Khedkar, I., Kanharia, Y., Jaswal, S. 2019. Success Prediction using Random Forest, CatBoost, XGBoost and AdaBoost for Kickstarter Campaigns, Proceedings of the Third International Conference on Computing Methodologies and Communication (ICCMC 2019), 27-29 March, Erode, India, 1170-1173.
  8. Jiang, H., 2021. Machine Learning Fundamentals, Cambridge University Press.
  9. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y., 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. 31st Conference on Neural Information Processing Systems (NIPS 2017).
  10. LI, Y., 2019. Credit Risk Prediction Based on Machine Learning Methods. 14th International Conference on Computer Science & Education (ICCSE 2019), Toronto, Canada, 19-21 August 2019, 1011-1013.
  11. Liang, Y., Wu, J., Wang, W., Cao, Y., Zhong, B., Chen, Z., Li,Z. 2019. Product Marketing Prediction based on XGboost and LightGBM Algorithm, Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition AIPR 2019, August 16–18, Beijing, China, 150-153.
  12. MELENDEZ, R., 2019. Credit Risk Analysis Applying Machine Learning Classification Models. Intelligent Computing - Proceedings of the Computing Conference CompCom 2019, Advances in Intelligent Systems and Computing, vol.997, 804-814.
  13. Pandey, T.N., Mohapatra, S.K., Jagadev, A.K., Dehuri, S., 2017. Credit Risk Analysis using Machine Learning Classifiers. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017), Chennai, India, 1-2 August 2017, 1850-1854.
  14. Pillai, S.G., Woodbury, J., Dikshit, N., Leider, A., Tappert, C.C., 2019. Credit Risk Analysis Applying Machine Learning Classification Models. Proceedings of the Future Technologies Conference (FTC) 2019, Advances in Intelligent Systems and Computing, vol.1069, 107-126.
  15. Ponsam, J.G., Bella Gracia, S.V.J., Geetha, G., Karpaselvi, S., Nimala, K., 2021. Credit Risk Analysis using LightGBM and a comparative study of popular algorithms. 4th International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 16-17 December, 634-641.
  16. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., Gulin, A., 2018. Catboost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems 31 (NeurIPS 2018).
  17. Qiu, Z., Li, Y., Ni, P., Li, G., 2019. Credit Risk Scoring Analysis Based on Machine Learning Models. 6th International Conference on Information Science and Control Engineering (ICISCE) Technologies, Shangai, China, 20-22 December 2019, 220-224.
  18. Tian, Z., Xiao, J., Feng, H., Wei, Y., 2020. Credit Risk Assessment based on Gradient Boosting Decision Tree. Procedia Computer Science, 174, 150-160.
  19. Turjo, A.A., Rahman, Y., Karim, S.M., Biswas, T.H., Dewan, I., Hossain, M.I., 2021. CRAM: A Credit Risk Assessment Model by Analyzing Different Machine Learning Algorithms. 4th International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 30-31 August, 125-130.
  20. Wang, Y., Zhang, Y., Lu, Y., Yu, X., 2020. A Comparative Assessment of Credit Risk Model Based on Machine Learning – a case study of bank loan data. Procedia Computer Science, 174, 141–149.
  21. Wang, Y., Lu, J., Qin, J., Zhang, C., Chen, Y., 2020. The Application Study of Credit Risk Model In Financial Institution via Machine-learning Algorithms. 7th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 18-20 December 2020, 1419-1428.
  22. Xie. Y., Xiang, W., Ji, M., Peng, J., Huang, Y. 2019. Application analysis of predicting monthly house rental based on XGBoost and LightGBM algorithms, Comput. Appl. Softw., 36(9), 151–155.
  23. Yalcin, M., Bagdatli Kalkan, S. 2022. Determining the best estimation model with tree-based machine learning methods: implementation on customer spendings for e-commerce websites, Advances and Applications in Statistics, 75, 91-109.
  24. Zhang, D., Gong, Y. 2020. The Comparison of LightGBM and XGBoost Coupling Factor Analysis and Prediagnosis of Acute Liver Failure, IEEE Access, 8, 220990-221003.
  25. Zhang, Y., Zhao, Z., Zheng, J. 2020. CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China, Journal of Hydrology, 588, 125087.
DOI: https://doi.org/10.2478/jamsi-2023-0001 | Journal eISSN: 1339-0015 | Journal ISSN: 1336-9180
Language: English
Page range: 5 - 18
Published on: Jun 9, 2023
Published by: University of Ss. Cyril and Methodius in Trnava
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2023 S. B. Coşkun, M. Turanli, published by University of Ss. Cyril and Methodius in Trnava
This work is licensed under the Creative Commons Attribution 4.0 License.