Two-Stage Classification Method for Individual Workout Status Prediction with Machine Learning Approach

References

• Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting, Finance and Management, 18(2–3), 59–88. https://doi.org/10.1002/isaf.325
   Google Scholar
• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
   Web of Science ®Google Scholar
• Bagherzadeh-Khiabani, F., Ramezankhani, A., Azizi, F., Hadaegh, F., Steyerberg, E. W., & Khalili, D. (2016). A tutorial on variable selection for clinical prediction models: Feature selection methods in data mining could improve the results. Journal of Clinical Epidemiology, 71, 76–85. https://doi.org/10.1016/j.jclinepi.2015.10.002
   PubMed Web of Science ®Google Scholar
• Bhat, G., Ryan, S. G., & Vyas, D. (2019). The implications of credit risk modeling for banks’ loan loss provisions and loan-origination procyclicality. Management Science, 65(5), 2116–2141. https://doi.org/10.1287/mnsc.2018.3041
   Web of Science ®Google Scholar
• Braun, H., & Chandler, J. S. (1987). Predicting stock market behavior through rule induction: An application of the learning‐from‐example approach. Decision Sciences, 18(3), 415–429. https://doi.org/10.1111/j.1540-5915.1987.tb01533.x
   Web of Science ®Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. https://doi.org/10.1007/BF00058655
   Web of Science ®Google Scholar
• Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11261–11272. https://doi.org/10.1016/j.eswa.2011.02.173
   Web of Science ®Google Scholar
• Dietterich, T. G. (2002). Ensemble learning. The Handbook of Brain Theory and Neural Networks, 2(1), 110–125.
   Google Scholar
• Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
   Web of Science ®Google Scholar
• Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4), 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2
   Web of Science ®Google Scholar
• Han, L. (2019). Controlling default contagion through small-world networks analysis. Procedia Computer Science, 154, 47–53. https://doi.org/10.1016/j.procs.2019.06.009
   Google Scholar
• Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (Vol. 2). springer.
   Google Scholar
• Hsu, H. H., Hsieh, C. W., & Lu, M. D. (2011). Hybrid feature selection by combining filters and wrappers. Expert Systems with Applications, 38(7), 8144–8150. https://doi.org/10.1016/j.eswa.2010.12.156
   Web of Science ®Google Scholar
• Jhee, W. C., & Lee, J. K. (1993). Performance of neural networks in managerial forecasting. Intelligent Systems in Accounting, Finance and Management, 2(1), 55–71. https://doi.org/10.1002/j.1099-1174.1993.tb00034.x
   Google Scholar
• Khan, U., & Lo, A. K. (2019). Bank lending standards and borrower accounting conservatism. Management Science, 65(11), 5337–5359. https://doi.org/10.1287/mnsc.2018.3094
   Web of Science ®Google Scholar
• Kittler, J., Hatef, M., Duin, R. P., & Matas, J. (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3), 226–239. https://doi.org/10.1109/34.667881
   Web of Science ®Google Scholar
• LaValley, M. P. (2008). Logistic regression. Circulation, 117(18), 2395–2399. https://doi.org/10.1161/CIRCULATIONAHA.106.682658
   PubMed Web of Science ®Google Scholar
• Liang, T. P., Chandler, J. S., Han, I., & Roan, J. (1992). An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks. Contemporary Accounting Research, 9(1), 306–328. https://doi.org/10.1111/j.1911-3846.1992.tb00883.x
   Google Scholar
• Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
   Google Scholar
• Lin, W. Y., Hu, Y. H., & Tsai, C. F. (2011). Machine learning in financial crisis prediction: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 421–436. https://doi.org/10.1109/TSMCC.2011.2170420
   Web of Science ®Google Scholar
• Min, J. H., & Lee, Y. C. (2008). A practical approach to credit scoring. Expert Systems with Applications, 35(4), 1762–1770. https://doi.org/10.1016/j.eswa.2007.08.070
   Web of Science ®Google Scholar
• Mitchell, T. M. (2007). Machine learning (Vol. 1). McGraw-hill.
   Google Scholar
• Muslim, M. A., & Dasril, Y. (2021). Company bankruptcy prediction framework based on the most influential features using XGBoost and stacking ensemble learning. International Journal of Electrical and Computer Engineering (IJECE), 11(6), 5549–5557. https://doi.org/10.11591/ijece.v11i6.pp5549-5557
   Google Scholar
• Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica: Journal of the Econometric Society, 29(3), 315–335. https://doi.org/10.2307/1909635
   Web of Science ®Google Scholar
• Nair, Y., Paim, L., Sabri, M. F., & Rahim, H. A. (2016). Predictors of bankruptcy probability among Malaysian civil servants: Examining the subjective measurement. Journal of Emerging Economies and Islamic Research, 4(1), 22–32. https://doi.org/10.24191/jeeir.v4i1.9076
   Google Scholar
• Noordin, N., Zakaria, Z., Sawal, M. Z. H. M., Ngah, K., & Hussin, Z. H. (2012). Bankruptcy among young executives in Malaysia. In 2012 ICEMM International Conference on Economics Marketing and Management, Hong Kong, China, (Vol. 28, pp.132–136).
   Google Scholar
• Odom, M. D., & Sharda, R. (1990, June). A neural network model for bankruptcy prediction. In 1990 IJCNN international joint conference on neural networks, San Diego, CA, USA (pp. 163–168). IEEE.
   Google Scholar
• Oh, Y. H. (2014). Evaluation and policy implications of personal loan modification programs in Korea. Available at SSRN 2792455.
   Google Scholar
• Patil, A. R., Chang, J., Leung, M. Y., & Kim, S. (2019). Analyzing high dimensional correlated data using feature ranking and classifiers. Computational and Mathematical Biophysics, 7(1), 98–120. https://doi.org/10.1515/cmb-2019-0008
   Google Scholar
• Peng, H., & Fan, Y. (2017). Feature selection by optimizing a lower bound of conditional mutual information. Information Sciences, 418, 652–667. https://doi.org/10.1016/j.ins.2017.08.036
   Web of Science ®Google Scholar
• Ryu, D., Park, J., & Nam, J.-H. (2021). Analysis on rescheduled debts of individual work-out program. Korea Deposit Insurance Corporation, 22(2–4). http://dx.doi.org/10.2139/ssrn.3995103
   Google Scholar
• Schapire, R. E. (1999, July). A brief introduction to boosting. In 1999 IJCAI International Joint Conference on Artificial Intelligence, Stockholm, Sweden, (Vol. 99, pp. 1401–1406).
   Google Scholar
• Scholkopf, B., Sung, K. K., Burges, C. J., Girosi, F., Niyogi, P., Poggio, T., & Vapnik, V. (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing, 45(11), 2758–2765. https://doi.org/10.1109/78.650102
   Web of Science ®Google Scholar
• Surkan, A. J., & Singleton, J. C. (1990, June). Neural networks for bond rating improved by multiple hidden layers. In 1990 IJCNN international joint conference on neural networks, San Diego, CA, USA (pp. 157–162). IEEE.
   Google Scholar
• Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38(7), 926–947. https://doi.org/10.1287/mnsc.38.7.926
   Web of Science ®Google Scholar
• Tsai, C. F. (2008). Financial decision support using neural networks and support vector machines. Expert Systems, 25(4), 380–393. https://doi.org/10.1111/j.1468-0394.2008.00449.x
   Web of Science ®Google Scholar
• Tsai, C. F. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46–58. https://doi.org/10.1016/j.inffus.2011.12.001
   Web of Science ®Google Scholar
• Xiong, T., Wang, S., Mayers, A., & Monga, E. (2013). Personal bankruptcy prediction by mining credit card data. Expert Systems with Applications, 40(2), 665–676. https://doi.org/10.1016/j.eswa.2012.07.072
   Web of Science ®Google Scholar