References
- Y. Li, "2018: What happened to the Chinese car industry?," Shanghai Enterprise, vol. 2019, no. 1, pp. 50-52, 2019.
- K. Wack, "Large banks assuming more risk in auto lending," 2019 [Online]. Available: https://www.americanbanker.com/news/large-banks-assuming-more-risk-in-auto-lending.
- H. E. Lim and S. G. Yeok, "Estimating the determinants of vehicle loan default in Malaysia: an exploratory study," International Journal of Management Studies, vol. 24, no. 1, pp. 73-90, 2017.
- H. Liu and J. Xu, "An empirical analysis of my country's auto consumer loan default: micro-evidence from commercial banks," South China Finance, vol. 1, no. 5, pp. 84-91, 2015.
- Y. Li and J. Ren, "Research on the causes of risk of default of personal vehicle loan in automobile finance company," China Market, vol. 2011, no. 2, pp. 138-140, 2011.
- Y. Shu and Q. Yang, "Research on auto loan default prediction based on large sample data model," Management Review, vol. 29, no. 9, pp. 59-71, 2017.
- K. Liu, "Application of random forest and logical regression model in default prediction," China Computer & Communication, vol. 2016, no. 21, pp. 111-112, 2016.
- A. Walks, "Driving the poor into debt? Automobile loans, transport disadvantage, and automobile dependence," Transport Policy, vol. 65, pp. 137-149, 2018. https://doi.org/10.1016/j.tranpol.2017.01.001
- M. Agrawal, A. Agrawal, and A. Raizada, "Predicting defaults in commercial vehicle loans using logistic regression: case of an Indian NBFC," CLEAR International Journal of Research in Commerce & Management, vol. 5, no. 5, pp. 22-28, 2014.
- P. M. Addo, D. Guegan, and B. Hassani, "Credit risk analysis using machine and deep learning models," Risks, vol. 6, no. 2, article no. 38, 2018. https://doi.org/10.3390/risks6020038
- R. Kazemi and A. Mosleh, "Improving default risk prediction using Bayesian model uncertainty techniques," Risk Analysis: An International Journal, vol. 32, no. 11, pp. 1888-1900, 2012. https://doi.org/10.1111/j.1539-6924.2012.01915.x
- X. Guo and Y Wu, "Analysis of investment decision in P2P lending based on support vector machine," China Sciencepaper Online, vol. 2017, no. 5, pp. 542-547, 2017.
- C. Jiang, Z. Wang, R. Wang, and Y. Ding, "Loan default prediction by combining soft information extracted from descriptive text in online peer-to-peer lending," Annals of Operations Research, vol. 266, no. 1, pp. 511-529, 2018. https://doi.org/10.1007/s10479-017-2668-z
- X. Deng and L. Zhao, "Speeding K-NN classification method based on data block mixed measurement," Computer and Modernization, vol. 2012, no. 12, pp. 47-50, 2016.
- V. Vapnik, The Nature of Statistical Learning Theory. New York, NY: Springer, 2010.
- N. Radhika, S. B. Senapathi, R. Subramaniam, R. Subramany, and K. N. Vishnu, "Pattern recognition based surface roughness prediction in turning hybrid metal matrix composite using random forest algorithm," Industrial Lubrication and Tribology, vol. 65, no. 5, pp. 311-319, 2013. https://doi.org/10.1108/ILT-02-2011-0015
- D. Yao, J. Yang, and X. Zhan, "Feature selection algorithm based on random forest," Journal of Jilin University (Engineering and Technology Edition), vol. 44, no. 1, pp. 137-141, 2014. https://doi.org/10.3321/j.issn:1671-5489.2006.01.027
- B. Gregorutti, B. Michel, and P. Saint-Pierre, "Correlation and variable importance in random forests," Statistics and Computing, vol. 27, no. 3, pp. 659-678, 2017. https://doi.org/10.1007/s11222-016-9646-1
- D. Denisko and M. M. Hoffman, "Classification and interaction in random forests," Proceedings of the National Academy of Sciences, vol. 115, no. 8, pp. 1690-1692, 2018. https://doi.org/10.1073/pnas.1800256115