References
- Basel Committee on Banking Supervision. (2005). Studies on the validation of internal rating systems. Working Paper. No. 14. Basel Committee on Banking Supervision.
- Bertolazzi, P., Felici, G., Festa, P., Fiscon, G., & Weitschek, E. (2016). Integer programming models for feature selection: New extensions and a randomized solution algorithm. European Journal of Operational Research, 250(2), 389–399. https://doi.org/https://doi.org/10.1016/j.ejor.2015.09.051
- Chen, Y. S. (2012). Classifying credit ratings for Asian banks using integrating feature selection and the CPDA-based rough sets approach. Knowledge-Based Systems, 26, 259–270. https://doi.org/https://doi.org/10.1016/j.knosys.2011.08.021
- Chen, F. L., & Li, F. C. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert Systems with Applications, 37(7), 4902–4909. https://doi.org/https://doi.org/10.1016/j.eswa.2009.12.025
- Chen, H., & Xiang, Y. (2017). The Study of Credit Scoring Model Based on Group Lasso. Procedia Computer Science, 122, 677–684. https://doi.org/https://doi.org/10.1016/j.procs.2017.11.423
- Chi, G., & Zhang, Z. (2017). Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method. Sustainability, 9(10), 1834. https://doi.org/https://doi.org/10.3390/su9101834
- Choi, H., Koo, J. Y., & Park, C. (2015). Fused least absolute shrinkage and selection operator for credit scoring. Journal of Statistical Computation and Simulation, 85(11), 2135–2147. https://doi.org/https://doi.org/10.1080/00949655.2014.922685
- Edla, D. R., Tripathi, D., Cheruku, R., & Kuppili, V. (2018). An Efficient Multi-layer Ensemble Framework with BPSOGSA-Based Feature Selection for Credit Scoring Data Analysis. Arabian Journal for Science & Engineering, 43(12), 6909–6928. https://doi.org/https://doi.org/10.1007/s13369-017-2905-4
- Erenguc, S. S., & Koehler, G. J. (1990). Survey of mathematical programming models and experimental results for linear discriminant analysis. Managerial and Decision Economics, 11(4), 215–225. https://doi.org/https://doi.org/10.1002/mde.4090110403
- Guo, S., He, H., & Huang, X. (2019). A multi-stage self-adaptive classifier ensemble model with application in credit scoring. IEEE Access., 7, 78549–78559. https://doi.org/https://doi.org/10.1109/ACCESS.2019.2922676.
- He, H., Zhang, W., & Zhang, S. (2018, May). A novel ensemble method for credit scoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105–117. https://doi.org/https://doi.org/10.1016/j.eswa.2018.01.012
- Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31(2), 231–240. https://doi.org/https://doi.org/10.1016/j.eswa.2005.09.024
- Koutanaei, F. N., Sajedi, H., & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for credit scoring. Journal of Retailing and Consumer Services, 27, 11–23. https://doi.org/https://doi.org/10.1016/j.jretconser.2015.07.003
- Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. https://doi.org/https://doi.org/10.1016/j.ejor.2015.05.030
- Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge & Data Engineering, 17(4), 491–502. https://doi.org/https://doi.org/10.1109/TKDE.2005.66
- Maldonado, S., & Weber, R. (2009). A wrapper method for feature selection using support vector machines. Information Sciences, 179(13), 2208–2217. https://doi.org/https://doi.org/10.1016/j.ins.2009.02.014
- Pavur, R., Wanarat, P., & Loucopoulos, C. (1997). Examination of the classificatory performance of MIP models with secondary goals for the two-group discriminant problem. Annals of Operations Research, 74, 173–189. https://doi.org/https://doi.org/10.1023/A:1018966203703
- Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/https://doi.org/10.1109/TPAMI.2005.159
- Sobehart, J. R., Keenan, S. C., & Stein, R. (2000). Benchmarking quantitative default risk models: a validation methodology. Moody’s Investors Service.
- Thomas, L., Crook, J., & Edelman, D. (2017). Credit scoring and its applications (2nd ed.). Siam.
- Tripathi, D., Cheruku, R., & Bablani, A. (2018). Relative performance evaluation of ensemble classification with feature reduction in credit scoring datasets. In D. R. Edla, P. Lingras, & V. Kuppili (Eds.), Advances in machine learning and data science (pp. 293–304). Springer.
- Tripathi, D., Edla, D. R., Cheruku, R., & Kuppili, V. (2019). A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification. Computational Intelligence, 35(2), 371–394. https://doi.org/https://doi.org/10.1111/coin.12200
- Wang, D., Zhang, Z., Bai, R., & Mao, Y. (2018). A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring. Journal of Computational and Applied Mathematics, 329, 307–321. https://doi.org/https://doi.org/10.1016/j.cam.2017.04.036
- Wu, X. (2006). Nonparametric statistics. China Statistics Press.
- Xu, D., Zhang, X., & Feng, H. (2019). Generalized fuzzy soft sets theory‐based novel hybrid ensemble credit scoring model. International Journal of Finance & Economics, 24(2), 903–921. https://doi.org/https://doi.org/10.1002/ijfe.1698
- Zhang, W., He, H., & Zhang, S. (2019). A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: An application in credit scoring. Expert Systems with Applications, 121(5), 221–232. https://doi.org/https://doi.org/10.1016/j.eswa.2018.12.020