Feature selection in credit risk modeling: an international evidence

References

• Al Amari, A. (2002). The credit evaluation process and the role of credit scoring: A case study of Qatar [Ph.D. Thesis]. University College Dublin.
   Google Scholar
• Ala'raj, M., & Abbod, M. F. (2016a). A new hybrid ensemble credit scoring model based on classifiers consensus system approach. Expert Systems with Applications, 64, 36–55. https://doi.org/https://doi.org/10.1016/j.eswa.2016.07.017
   Web of Science ®Google Scholar
• Ala'raj, M., & Abbod, M. F. (2016b). Classifier consensus system approach for credit scoring. Knowledge-Based Systems, 104, 89–105. https://doi.org/https://doi.org/10.1016/j.knosys.2016.04.013
   Web of Science ®Google Scholar
• Al-Hadeethi, H., Abdulla, S., Diykh, M., Deo, R. C., & Green, J. H. (2020). Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis application. Expert Systems with Applications, 161, 113676. https://doi.org/https://doi.org/10.1016/j.eswa.2020.113676
   Web of Science ®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/https://doi.org/10.1111/j.1540-6261.1968.tb00843.x http://links.jstor.org/sici?sici=00221082%28196809%2923%3A4%3C589%3AFRDAAT%3E2.0.CO%3B2-R.
   Web of Science ®Google Scholar
• Amendola, A., Restaino, M., & Sensini, L. (2011). Variable selection in default risk models. The Journal of Risk Model Validation, 5 (1), 3–19. https://doi.org/https://doi.org/10.21314/JRMV.2011.066
   Web of Science ®Google Scholar
• Arora, S., & Anand, P. (2019). Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 116, 147–160. https://doi.org/https://doi.org/10.1016/j.eswa.2018.08.051
   Web of Science ®Google Scholar
• Arora, N., & Kaur, P. D. (2020). A Bolasso based consistent feature selection enabled random forest classification algorithm: An application to credit risk assessment. Applied Soft Computing Journal, 86, 105936. https://doi.org/https://doi.org/10.1016/j.asoc.2019.105936
   Web of Science ®Google Scholar
• Bao, W., Lianju, N., & Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications , 128, 301–315. https://doi.org/https://doi.org/10.1016/j.eswa.2019.02.033
   Web of Science ®Google Scholar
• Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/https://doi.org/10.2307/2490171
   Web of Science ®Google Scholar
• Beaver, W. H., McNichols, M. F., & Rhie, J. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies, 10(1), 93–122. https://doi.org/http://dx.doi.org/10.2139/ssrn.634921
   Web of Science ®Google Scholar
• Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). MOA: Massive Online Analysis. Journal of Machine Learning Research, 11, 1601–1604. https://dl.acm.org/doi/10.5555/1756006.1859903
   Web of Science ®Google Scholar
• Bishop, C. M. (1995). Neural Networks for Pattern Recognition (Advanced Texts in Econometrics (Paperback). Oxford University Press.
   Google Scholar
• Boyacioglu, M. A., Kara, Y., & Baykan, O. K. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: a comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355–3366. https://doi.org/https://doi.org/10.1016/j.eswa.2008.01.003
   Web of Science ®Google Scholar
• Breiman, L. (1995). Better subset regression using the nonnegative garotte. Technometrics, 37(4), 373–384. https://doi.org/https://doi.org/10.2307/1269730
   Web of Science ®Google Scholar
• Breiman, L. (1996). Heuristics of instability and stabilization in model selection. Annals of Statistics, 24, 2297–2778. https://doi.org/https://doi.org/10.1214/aos/1032181158
   Web of Science ®Google Scholar
• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. The Wadsworth.
   Google Scholar
• Briand, L. C., Freimut, B., & Vollei, F. (2004). Using multiple adaptive regression splines to support decision making in code inspections. Journal of Systems and Software, 73 (2), 205–217. https://doi.org/https://doi.org/10.1016/j.jss.2004.01.015
   Web of Science ®Google Scholar
• Campbell, J., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899–2939. https://doi.org/https://doi.org/10.1111/j.1540-6261.2008.01416.x
   Web of Science ®Google Scholar
• Chang, Y.-C., Chang, K.-H., & Wu, G.-J. (2018). Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Applied Soft Computing Journal, 73, 914–920. https://doi.org/https://doi.org/10.1016/j.asoc.2018.09.029
   Web of Science ®Google Scholar
• Chava, S., & Jarrow, R. A. (2004). Bankruptcy prediction with industry effects. Review of Finance, 8(4), 537–569. https://doi.org/http://dx.doi.org/10.2139/ssrn.287474
   Google Scholar
• Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, W. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321–378. https://doi.org/https://doi.org/10.1613/jair.953
   Web of Science ®Google Scholar
• Chi, G., Abedin, M. Z., & Moula, F. E. (2017). Modeling credit approval data with neural networks: an experimental investigation and optimization. Journal of Business Economics and Management, 18 (2), 224–240. https://doi.org/https://doi.org/10.3846/16111699.2017.1280844
   Web of Science ®Google Scholar
• Chi, G., Uddin, M. S., Abedin, M. Z., & Yuan, k. (2019). Hybrid model for credit risk prediction: An application of neural network approaches. International Journal on Artificial Intelligence Tools, 28(05), 1–33. https://doi.org/https://doi.org/10.1142/S0218213019500179
   Web of Science ®Google Scholar
• Chi, G., Yu, S., & Zhou, Y. (2019a). A novel credit evaluation model based on the maximum discrimination of evaluation results. Emerging Markets Finance and Trade, 56(11), 2543–2562. https://doi.org/https://doi.org/10.1080/1540496X.2019.1643717
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273–297. https://doi.org/https://doi.org/10.1007/BF00994018
   Web of Science ®Google Scholar
• Danenas, P., & Garsva, G. (2015). Selection of support vector machines based classifiers for credit risk domain. Expert Systems with Applications, 42(6), 3194–3204. https://doi.org/https://doi.org/10.1016/j.eswa.2014.12.001
   Web of Science ®Google Scholar
• Demšar, J. (2006). Statistical comparisons of classifiers over multiple datasets. The Journal of Machine Learning Research, 7, 1–30.
   Web of Science ®Google Scholar
• Desai, V. S., Crook, J. N., & Overstreet, G. A. A (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95 (1), 24–37. https://doi.org/https://doi.org/10.1016/0377-2217(95)00246-4
   Web of Science ®Google Scholar
• Dimla, D. E., & Lister, P. M. (2000). On-line metal cutting tool condition monitoring. II: tool-state classification using multilayer perceptron neural networks. International Journal of Machine Tools and Manufacture, 40 (5), 769–781. https://doi.org/https://doi.org/10.1016/S0890-6955(99)00085-1 https://doi.org/https://doi.org/10.1016/S0890-6955(99)00085-1
   Web of Science ®Google Scholar
• Ding, Y., Song, X., & Zen, Y. (2008). Forecasting financial condition of Chinese listed companies based on support vector machine. Expert Systems with Applications, 34(4), 3081–3089. https://doi.org/https://doi.org/10.1016/j.eswa.2007.06.037
   Web of Science ®Google Scholar
• Drown, D. J., Khoshgoftaar, T. M., & Seliya, N. (2009). Evolutionary sampling and software quality modeling of high-assurance systems. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39(5), 1097–1107. https://doi.org/https://doi.org/10.1109/TSMCA.2009.2020804
   Web of Science ®Google Scholar
• Du, S. S., Zhai, X., Poczos, B., & Singh, A. (2019). Gradient Descent Provably Optimizes Over-parameterized Neural Networks. International Conference on Learning Representations (ICLR).
   Google Scholar
• Dunn, O. J. (1961). Multiple Comparisons among Means. Journal of the American Statistical Association, 56(293), 52–64. https://doi.org/https://doi.org/10.1080/01621459.1961.10482090
   Web of Science ®Google Scholar
• Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. The Annals of Statistics, 32(2), 407–499. https://projecteuclid.org/euclid.aos/1083178935 https://doi.org/https://doi.org/10.1214/009053604000000067
   Web of Science ®Google Scholar
• Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348–1360. https://doi.org/https://doi.org/10.1198/016214501753382273
   Web of Science ®Google Scholar
• Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19 (1), 1–67. https://doi.org/https://doi.org/10.1214/aos/1176347963
   Web of Science ®Google Scholar
• Friedman, M. (1940). A comparison of alternative tests of significance for the problem of rankings. The Annals of Mathematical Statistics, 11(1), 86–92. https://doi.org/https://doi.org/10.1214/aoms/1177731944
   Google Scholar
• García, V., Marqués, A. I., & Sánchez, J. S. (2015). An insight into the experimental design for credit risk and corporate bankruptcy prediction systems. Journal of Intelligent Information Systems, 44(1), 159–189. https://doi.org/https://doi.org/10.1007/s10844-014-0333-4
   Web of Science ®Google Scholar
• Gately, E. (1996). Neural Networks for Financial Forecasting: Top Techniques for Designing and Applying the Latest Trading Systems. John Wiley & Sons, Inc.
   Google Scholar
• Hand, D. J., & Jacka, S. D. (1998). Statistics in Finance. Arnold Applications of Statistics: London.
   Google Scholar
• Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference, and prediction. Mathematical Intelligencer, 27 (2), 83–85.
   Google Scholar
• He, H., & Garcia, E. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21, 1263–1284. https://doi.org/https://doi.org/10.1109/TKDE.2008.239
   Web of Science ®Google Scholar
• Hu, L., Gao, W., Zhao, K., Zhang, P., & Wang, F. (2018). Feature selection considering two types of feature relevancy and feature interdependency. Expert Systems with Applications, 93, 423–434. https://doi.org/https://doi.org/10.1016/j.eswa.2017.10.016
   Web of Science ®Google Scholar
• Huang, Z., Chen, H., Hsu, C. J., Chen, W. H., & Wu, S. (2004). Credit rating analysis with support vector machines and neural networks a market comparative study. Decision Support Systems, 37(4), 543–558. https://doi.org/https://doi.org/10.1016/S0167-9236(03)00086-1
   Web of Science ®Google Scholar
• Huang, C. L., Chen, M. C., & Wang, C. J. (2007). Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications, 33(4), 847–856. https://doi.org/https://doi.org/10.1016/j.eswa.2006.07.007
   Web of Science ®Google Scholar
• Jalal, M., Arabali, P., Grasley, Z., Bullard, J. W., & Jalal, H. (2020). Behavior assessment, regression analysis and support vector machine (SVM) modeling of waste tire rubberized concrete. Journal of Cleaner Production, 273, 122960. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.122960
   Web of Science ®Google Scholar
• Jiang, H., Ching, W., Yiu, K. F. C., & Qiu, Y. (2018). Stationary Mahalanobis kernel SVM for credit risk evaluation. Applied Soft Computing, 71, 407–417. https://doi.org/https://doi.org/10.1016/j.asoc.2018.07.005
   Web of Science ®Google Scholar
• Jiang, Y., & Jones, S. (2018). Corporate distress prediction in China: A machine learning approach. Accounting & Finance, 58 (4), 1063–1109. https://doi.org/https://doi.org/10.1111/acfi.12432
   Web of Science ®Google Scholar
• Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical models. Journal of Business Finance & Accounting, 44 (1–2), 3–34. https://doi.org/https://doi.org/10.1111/jbfa.12218
   Web of Science ®Google Scholar
• Jones, S. (2017). Corporate bankruptcy prediction: a high dimensional analysis. Review of Accounting Studies, 22 (3), 1366–1422. https://doi.org/https://doi.org/10.1007/s11142-017-9407-1
   Web of Science ®Google Scholar
• Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking & Finance, 56, 72–85. https://doi.org/https://doi.org/10.1016/j.jbankfin.2015.02.006
   Web of Science ®Google Scholar
• Jones, S., & Wang, T. (2019). Predicting private company failure: A multi-class analysis. Journal of International Financial Markets, Institutions & Money, 61, 161–188. https://doi.org/https://doi.org/10.1016/j.intfin.2019.03.004.
   Web of Science ®Google Scholar
• Kim, K. J., & Ahn, H. (2012). A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research, 39, 1800–1811. https://doi.org/https://doi.org/10.1016/j.cor.2011.06.023
   Web of Science ®Google Scholar
• Kouziokas, G. N. (2020). A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting. Engineering Applications of Artificial Intelligence, 92, 103650. https://doi.org/https://doi.org/10.1016/j.engappai.2020.103650
   Web of Science ®Google Scholar
• Kozodoi, N., Lessmann, S., Papakonstantinou, K., Gatsoulis, Y., & Baesens, B. (2019). A multi-objective approach for profit-driven feature selection in credit scoring. Decision Support Systems, 120, 106–117. https://doi.org/https://doi.org/10.1016/j.dss.2019.03.011
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Lewis, E. M. (1992). An Introduction to Credit Scoring. Fair, Isaac & Co., Inc.
   Google Scholar
• Liang, D., Tsai, C.-F., & Wu, H.-T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289–297. https://doi.org/https://doi.org/10.1016/j.knosys.2014.10.010
   Web of Science ®Google Scholar
• Lin, W.-Y., Hu, Y.-H., & Tsai, C.-F. (2012). 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/https://doi.org/10.1109/TSMCC.2011.2170420
   Web of Science ®Google Scholar
• Liu, Y., & Schumann, M. (2005). Data mining feature selection for credit-scoring models. Journal of the Operational Research Society, 56(9), 1099–1108. https://doi.org/https://doi.org/10.1057/palgrave.jors.2601976
   Web of Science ®Google Scholar
• López, J., & Maldonado, S. (2019). Profit-based credit scoring based on robust optimization and feature selection. Information Sciences , 500, 190–202. https://doi.org/https://doi.org/10.1016/j.ins.2019.05.093
   Web of Science ®Google Scholar
• Luo, J., Yan, X., & Tian, Y. (2020). Unsupervised quadratic surface support vector machine with application to credit risk assessment. European Journal of Operational Research, 280(3), 1008–1017. https://doi.org/https://doi.org/10.1016/j.ejor.2019.08.010
   Web of Science ®Google Scholar
• Mahajan, V., Jain, A. K., & Bergier, M. (1977). Parameter estimation in marketing models in the presence of multicollinearity: an application of ridge regression. Journal of Marketing Research, 14 (4), 586–591. https://doi.org/https://doi.org/10.1177/002224377701400419
   Web of Science ®Google Scholar
• Maldonado, S., Bravo, C., López, J., & Pérez, J. (2017). Integrated framework for profit-based feature selection and SVM classification in credit scoring. Decision Support Systems, 104, 113–121. https://doi.org/https://doi.org/10.1016/j.dss.2017.10.007
   Web of Science ®Google Scholar
• Marqués, A. I., García, V., & Sánchez, J. S. (2012a). Exploring the behaviour of base classifiers in credit scoring ensembles. Expert Systems with Applications, 39 (11), 10244–10250. https://doi.org/https://doi.org/10.1016/j.eswa.2012.02.092
   Web of Science ®Google Scholar
• Marqués, A. I., García, V., & Sánchez, J. S. (2012b). Two-level classifier ensembles for credit risk assessment. Expert Systems with Applications, 39 (12), 10916–10922. https://doi.org/https://doi.org/10.1016/j.eswa.2012.03.033[Mismatch]
   Web of Science ®Google Scholar
• Mason, C. H., William, D., & Perreault, J. R. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28 (3), 268–280. https://doi.org/https://doi.org/10.2307/3172863 https://doi.org/https://doi.org/10.1177/002224379102800302
   Web of Science ®Google Scholar
• Masters, T. (1995). Advanced Algorithms for Neural Networks: A C++ Sourcebook. John Wiley & Sons, Inc.
   Google Scholar
• Meier, L., Geer, S., & Bühlmann, P. (2008). The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology)), 70(1), 53–71. https://doi.org/https://doi.org/10.1111/j.1467-9868.2007.00627.x
   Web of Science ®Google Scholar
• Min, J. H., & Lee, Y. C. (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function Parameters. Expert Systems with Applications, 28(4), 603–614. https://doi.org/https://doi.org/10.1016/j.eswa.2004.12.008
   Web of Science ®Google Scholar
• Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18 (1), 109–131. https://doi.org/https://doi.org/10.2307/2490395
   Web of Science ®Google Scholar
• Ping, Y., & Yongheng, L. (2011). Neighborhood rough set and SVM based hybrid credit scoring classifier. Expert Systems with Applications, 38(9), 11300–11304. https://doi.org/https://doi.org/10.1016/j.eswa.2011.02.179
   Web of Science ®Google Scholar
• Reed, R. D., & Marks, R. J. (1999). Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks. The MIT Press.
   Google Scholar
• Shin, K. S., Lee, T. S., & Kim, H. J. (2005). An application of support vector machines in bankruptcy prediction model. Expert Systems with Applications, 28(1), 127–135. https://doi.org/https://doi.org/10.1016/j.eswa.2004.08.009
   Web of Science ®Google Scholar
• Shumway, T. (2001). Forecasting bankruptcy more accurately: a simple hazard model. The Journal of Business, 74(1), 101–124. https://doi.org/http://dx.doi.org/10.2139/ssrn.171436
   Web of Science ®Google Scholar
• Swets, J. A., Dawes, R. M., & Monahan, J. (2000). Better decisions through science. Scientific American, 283 (4), 82–87. https://doi.org/https://doi.org/10.1038/scientificamerican1000-82
   PubMed Web of Science ®Google Scholar
• Thomas, L. C., Edelman, D. B., & Crook, L. N. (2002). Credit Scoring and Its Applications. Philadelphia. Society for Industrial and Applied Mathematics.
   Google Scholar
• Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics & Finance ,51, 510–526. https://doi.org/https://doi.org/10.1016/j.iref.2017.07.025
   Web of Science ®Google Scholar
• Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89–100. https://doi.org/https://doi.org/10.1016/j.jbankfin.2014.12.003
   Web of Science ®Google Scholar
• Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
   Web of Science ®Google Scholar
• Trippi, R. R., & Turban, E. (1993). Neural Networks in Finance and Investing: Using Artificial Intelligence to Improve Real-World Performance. IRWIN.
   Google Scholar
• Tsai, C.-F., & Sung, Y.-T. (2020). Ensemble feature selection in high dimension, low sample size datasets: Parallel and serial combination approaches. Knowledge-Based Systems, 203, 106097. https://doi.org/https://doi.org/10.1016/j.knosys.2020.106097
   Web of Science ®Google Scholar
• Uddin, M. S., Chi, G., Al Janabi, M. A. M., & Habib, T. (2020b). Leveraging random forest in micro-enterprises credit risk modelling for accuracy and interpretability. International Journal of Finance & Economics, 1–17. https://doi.org/https://doi.org/10.1002/ijfe.2346
   Google Scholar
• Uddin, M. S., Chi, G., Habib, T., & Zhou, Y. (2020a). An alternative statistical framework for credit default prediction. Journal of Risk Model Validation, 14 (2), 1–36. https://doi.org/https://doi.org/10.21314/JRMV.2020.220
   Web of Science ®Google Scholar
• Vinod, H. D. (1978). A survey of ridge regression and related techniques for improvements over ordinary least squares. The Review of Economics and Statistics, 60 (1), 121–131. https://ssrn.com/abstract=1750091 https://doi.org/https://doi.org/10.2307/1924340
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Wei, G., Zhao, J., Feng, Y., He, A., & Yu, J. (2020). A novel hybrid feature selection method based on dynamic feature importance. Applied Soft Computing Journal, 93, 106337. https://doi.org/https://doi.org/10.1016/j.asoc.2020.106337
   Web of Science ®Google Scholar
• West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27 (11–12), 1131–1152. https://doi.org/https://doi.org/10.1016/S0305-0548(99)00149-5. https://doi.org/https://doi.org/10.1016/S0305-0548(99)00149-5
   Web of Science ®Google Scholar
• Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z.-H., Steinbach, M., Hand, D. J., & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37. https://doi.org/https://doi.org/10.1007/s10115-007-0114-2
   Web of Science ®Google Scholar
• Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction on SVM and MDA methods: the case of Chinese listed companies. Quality & Quantity, 45, 671–686. https://doi.org/https://doi.org/10.1007/s11135-010-9376-y
   Google Scholar
• Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: A fast correlation-based filter solution. International Conference on Machine Learning, 2, 856–863.
   Google Scholar
• Yu, L., Zhou, R., Tang, L., & Chen, R. (2018). A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data. Applied Soft Computing, 69, 192–202. https://doi.org/https://doi.org/10.1016/j.asoc.2018.04.049
   Web of Science ®Google Scholar
• Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004). Small Business Credit Scoring: A Comparison of Logistic Regression, Neural Networks, and Decision Tree Models. 26th International Conference on Information Technology Interfaces, Croatia. https://doi.org/https://doi.org/10.1109/ITI.2004.241696
   Google Scholar
• Zheng, K., Chen, Y., Jiang, Y., & Qiao, S. (2020). A SVM based ship collision risk assessment algorithm. Ocean Engineering, 202, 107062. https://doi.org/https://doi.org/10.1016/j.oceaneng.2020.107062
   Web of Science ®Google Scholar
• Zhong, H., Miao, C., Shen, Z., & Feng, Y. (2014). Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings. Neurocomputing , 128, 285–295. https://doi.org/https://doi.org/10.1016/j.neucom.2013.02.054
   Web of Science ®Google Scholar
• Zhou, L., Lai, K. K., & Yu, L. (2010). Least squares support vector machines ensemble models for credit scoring. Expert Systems with Applications, 37(1), 127–133. https://doi.org/https://doi.org/10.1016/j.eswa.2009.05.024
   Web of Science ®Google Scholar
• Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418–1429. https://doi.org/https://doi.org/10.1198/016214506000000735
   Web of Science ®Google Scholar