Machine learning models and cost-sensitive decision trees for bond rating prediction

References

• Abdel-Zaher, A. M., & Eldeib, A. M. (2016). Breast cancer classification using deep belief networks. Expert Systems with Applications, 46, 139–144. doi:10.1016/j.eswa.2015.10.015
   Web of Science ®Google Scholar
• Abellán, J., & Castellano, J. G. (2017). A comparative study on base classifiers in ensemble methods for credit scoring. Expert Systems with Applications, 73, 1–10. doi:10.1016/j.eswa.2016.12.020
   Web of Science ®Google Scholar
• Abellán, J., & Mantas, C. J. (2014). Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 41(8), 3825–3830. doi:10.1016/j.eswa.2013.12.003
   Web of Science ®Google Scholar
• Abiyev, R. (2014). Credit rating using Type-2 fuzzy neural networks. Mathematical Problems in Engineering, 2014, 1. doi:10.1155/2014/460916
   Web of Science ®Google Scholar
• Ackley, D. H., Hinton, G. E., & Sejnowski, J. (1985). A learning algorithm for Boltzmann machines. Cognitive Science, 9(1), 147–169. doi:10.1016/S0364-0213(85)80012-4
   Web of Science ®Google Scholar
• Adamu Teshome, D., & Rao, V. S. (2014). A cost sensitive machine learning approach for intrusion detection. Global Journal of Computer Science and Technology, 14(6), 1–8.
   Google Scholar
• Affonso, C., Rossi, A. L. D., Vieira, F. H. A., de Carvalho, A. C. P., & de, L. F. (2017). Deep learning for biological image classification. Expert Systems with Applications, 85, 114–122. doi:10.1016/j.eswa.2017.05.039
   Web of Science ®Google Scholar
• Afonso, A., Furceri, D., & Gomes, P. (2012). Sovereign credit ratings and financial markets linkages: Application to European data. Journal of International Money and Finance, 31(3), 606–638. doi:10.1016/j.jimonfin.2012.01.016
   Web of Science ®Google Scholar
• Afonso, A., Gomes, P., & Taamouti, A. (2014). Sovereign credit ratings, market volatility, and financial gains. Computational Statistics & Data Analysis, 76, 20–33. doi:10.1016/j.csda.2013.09.028
   Web of Science ®Google Scholar
• Ala’raj, M., & Abbod, M. F. (2016). Classifiers consensus system approach for credit scoring. Knowledge-Based Systems, 104, 1–17.
   Web of Science ®Google Scholar
• Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110–122. doi:10.1016/j.dss.2007.12.002
   Web of Science ®Google Scholar
• Alsakka, R., & Gwilym, O. (2012). Foreign exchange market reactions to sovereign credit news. Journal of International Money and Finance, 31(4), 845–864. doi:10.1016/j.jimonfin.2012.01.007
   Web of Science ®Google Scholar
• Ando, T. (2014). Bayesian corporate bond pricing and credit default swap premium models for deriving default probabilities and recovery rates. Journal of the Operational Research Society, 65(3), 454–465. doi:10.1057/jors.2013.16
   Web of Science ®Google Scholar
• Atiya, A. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929–935. doi:10.1109/72.935101
   PubMed Web of Science ®Google Scholar
• Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015). Expert systems with applications example-dependent cost-sensitive decision trees. Expert Systems with Applications, 42(19), 6609–6619. doi:10.1016/j.eswa.2015.04.042
   Web of Science ®Google Scholar
• Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Application, 83, 405–417. doi:10.1016/j.eswa.2017.04.006
   Web of Science ®Google Scholar
• Bequé, A., Coussement, K., Gayler, R., & Lessmann, S. (2017). Approaches for credit scorecard calibration: An empirical analysis. Knowledge-Based Systems, 134, 213–227. doi:10.1016/j.knosys.2017.07.034
   Web of Science ®Google Scholar
• Borensztein, E., Cowan, K., & Valenzuela, P. (2013). Sovereign ceiling ‘lite’? The impact of sovereign ratings on corporate. Journal of Banking & Finance, 37, 4014–4024. doi:10.1016/j.jbankfin.2013.07.006
   Web of Science ®Google Scholar
• Bradford, J. P., Kunz, C., Kohavi, R., Brunk, C., & Brodley, C. E. (1998). Pruning decision trees with misclassication costs. In Claire Nédellec and Céline Rouveirol (Eds.), ECML-98. Proceedings of the 10th European Conference on Machine Learning (pp. 131–136). Berlin: Springer.
   Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
   Google Scholar
• Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and regression trees. Boca Raton, Florida: Chapman and Hall-CRC.
   Google Scholar
• Brezigar-Masten, A., & Masten, I. (2012). CART-based selection of bankruptcy predictors for the logit model. Expert Systems with Applications, 39(11), 10153–10159. doi:10.1016/j.eswa.2012.02.125
   Web of Science ®Google Scholar
• Brooks, R., Faff, R. W., Hillier, D., & Hillier, J. (2004). The national market impact of sovereign rating changes. Journal of Banking and Finance, 28(1), 233–250. doi:10.1016/S0378-4266(02)00406-5
   Web of Science ®Google Scholar
• Camba-Méndez, G., & Serwa, D. (2016). Market perception of sovereign credit risk in the euro area during the financial crisis. North American Journal of Economics and Finance, 37, 168–189. doi:10.1016/j.najef.2016.04.002
   Web of Science ®Google Scholar
• Cano, J., Aljohani, N. R., Abbasi, R. A., Alowidbi, J. S., & García, S. (2017). Prototype selection to improve monotonic nearest neighbor. Engineering Applications of Artificial Intelligence, 60, 128–135. doi:10.1016/j.engappai.2017.02.006
   Web of Science ®Google Scholar
• Cao, L., Guan, L., & Jingqing, Z. (2006). Bond rating using support vector machine. Intelligent Data Analysis, 10(3): 285–296.
   Google Scholar
• Chauchat, J., Rakotomalala, R., Carloz, M., & Pelletier, C. (2001). Targeting customer groups using gain and cost matrix: A marketing application. PKDD 2001. Proceedings of Data Mining for Marketing Applications Workshop, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases (pp. 1–13).
   Google Scholar
• Chauhan, N., Ravi, V., & Karthik Chandra, D. (2009). Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks. Expert Systems with Applications, 36(4), 7659–7665. doi:10.1016/j.eswa.2008.09.019
   Web of Science ®Google Scholar
• Chen, C.-C., & Li, S.-T. (2014). Credit rating with a monotonicity-constrained support vector machine model. Expert Systems with Applications, 41(16), 7235–7247. doi:10.1016/j.eswa.2014.05.035
   Web of Science ®Google Scholar
• Chen, F., & Li, F. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert Systems with Applications, 37(7), 4902–4909. doi:10.1016/j.eswa.2009.12.025
   Web of Science ®Google Scholar
• Chen, M. Y. (2011). Bankruptcy prediction in firms with statistical and intelligent techniques and a comparison of evolutionary computation approaches. Computers and Mathematics with Applications, 62(12), 4514–4524. doi:10.1016/j.camwa.2011.10.030
   Web of Science ®Google Scholar
• Chen, N., Ribeiro, B., Vieira, A. S., Duarte, J., & Neves, J. C. (2011). A genetic algorithm-based approach to cost-sensitive bankruptcy prediction. Expert Systems with Applications, 38(10), 12939–12945. doi:10.1016/j.eswa.2011.04.090
   Web of Science ®Google Scholar
• Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction : Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187–205. doi:10.1016/j.eswa.2017.04.030
   Web of Science ®Google Scholar
• Cortes, C., & Vapnik, V. Sep. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. doi:10.1007/BF00994018
   Web of Science ®Google Scholar
• Crabtree, A. D., & Maher, J. J. (2005). Earnings predictability, bond ratings, and bond yields. Review of Quantitative Finance and Accounting, 25(3), 233–253. doi:10.1007/s11156-005-4766-2
   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. doi:10.1016/j.eswa.2014.12.001
   Web of Science ®Google Scholar
• Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
   Web of Science ®Google Scholar
• Demyanyk, Y., & Hasan, I. (2010). Financial crises and bank failures: A review of prediction methods. Omega, 38(5), 315–324. doi:10.1016/j.omega.2009.09.007
   Web of Science ®Google Scholar
• Dixon, M., Klabjan, D., & Bang, J. H. (2015). Implementing deep neural networks for financial market prediction on the Intel Xeon Phi. Proceedings of the 8th Workshop on High Performance Computational Finance (WHPCF’15), 1–6. New York, NY, USA.
   Google Scholar
• Du Jardin, P. (2010). Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy. Neurocomputing, 73(10–12), 2047–2060. doi:10.1016/j.neucom.2009.11.034
   Web of Science ®Google Scholar
• Du Jardin, P. (2016). A two-stage classification technique for bankruptcy prediction. European Journal of Operational Research, 254(1), 236–252. doi:10.1016/j.ejor.2016.03.008
   Web of Science ®Google Scholar
• Du Jardin, P. (2017). Dynamics of firm financial evolution and bankruptcy prediction. Expert Systems with Applications, 75, 25–43. doi:10.1016/j.eswa.2017.01.016
   Web of Science ®Google Scholar
• Elfwing, S., Uchibe, E., & Doya, K. (2015). Expected energy-based restricted Boltzmann machine forclassification. Neural Networks, 64, 29–38. doi:10.1016/j.neunet.2014.09.006
   PubMed Web of Science ®Google Scholar
• Fedorova, E., Gilenko, E., & Dovzhenko, S. (2013). Bankruptcy prediction for Russian companies: Application of combined classifiers. Expert Systems with Applications, 40(18), 7285–7293. doi:10.1016/j.eswa.2013.07.032
   Web of Science ®Google Scholar
• Ferreira, M. A., & Gama, P. M. (2007). Does sovereign debt ratings news spill over to international stock markets? Journal of Banking and Finance, 31(10), 3162–3182. doi:10.1016/j.jbankfin.2006.12.006
   Web of Science ®Google Scholar
• Florez-Lopez, R. (2007). Modelling of insurers' rating determinants. An application of machine learning techniques and statistical models. European Journal of Operational Research, 183(3), 1488–1512. doi:10.1016/j.ejor.2006.09.103
   Google Scholar
• Freund, Y. (1990). Boosting a weak learning algorithm by majority. In Colt'90: The third annual workshop on computational learning theory (pp. 202–216). Rochester, New York, USA: Morgan Kaufmann.
   Google Scholar
• Freund, Y. (1995). Boosting a weak learning algorithm by majority. Information and Computation, 121(2), 256–285. doi:10.1006/inco.1995.1136
   Web of Science ®Google Scholar
• Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. Proceedings of the 13th international conference on machine learning (pp. 148–156). Bari, Italy: Morgan Kaufmann.
   Google Scholar
• Geng, R., Bose, I., & Chen, X. (2015). Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research, 241(1), 236–247. doi:10.1016/j.ejor.2014.08.016
   Web of Science ®Google Scholar
• Gültekin-Karakaş, D., Hisarcıklılar, M., & Öztürk, H. (2011). Sovereign risk ratings: Biased toward developed countries? Emerging Markets Finance and Trade, 47(suppl. 2), 69–87. doi:10.2753/REE1540-496X4703S204
   Web of Science ®Google Scholar
• Harris, T. (2015). Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42(2), 741–750. doi:10.1016/j.eswa.2014.08.029
   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. doi:10.1016/S0167-9236(03)00086-1
   Web of Science ®Google Scholar
• Jackson, J. D., & Boyd, J. W. (1988). A statistical approach to modeling the behavior of bond raters. Journal of Behavioral Economics, 17(3), 173–193. doi:10.1016/0090-5720(88)90008-3
   Google Scholar
• Japkowicz, N., & Shah, M. (2011). Evaluating Learning Algorithms – A Classification Perspective. Cambridge: Cambridge University Press.
   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 and Finance, 56, 72–85. doi:10.1016/j.jbankfin.2015.02.006
   Web of Science ®Google Scholar
• Kaminsky, G., & Schmukler, S. (2002). Emerging market instability: Do sovereign ratings affect country risk and stock returns? The World Bank Economic Review, 16(2), 171–195. doi:10.1093/wber/16.2.171
   Web of Science ®Google Scholar
• Karels, G. V., & Prakash, A. J. (1987). Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance & Accounting, 14(4), 573–593. doi:10.1111/j.1468-5957.1987.tb00113.x
   Google Scholar
• 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. doi:10.1016/j.jretconser.2015.07.003
   Web of Science ®Google Scholar
• Krauss, C., Anh, X., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S & P 500 R. European Journal of Operational Research, 259(2), 689–702.
   Web of Science ®Google Scholar
• Krawczyk, B., Woźniak, M., & Schaefer, G. (2014). Cost-sensitive decision tree ensembles for effective imbalanced classification. Applied Soft Computing Journal, 14(C), 554–562. doi:10.1016/j.asoc.2013.08.014
   Web of Science ®Google Scholar
• Li, F., Zhang, X., Zhang, X., Du, C., Xu, Y., & Tian, Y. C. (2018). Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets. Information Sciences, 422, 242–256. doi:10.1016/j.ins.2017.09.013
   Web of Science ®Google Scholar
• Li, H., Sun, J., & Wu, J. (2010). Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods. Expert Systems with Applications, 37(8), 5895–5904. doi:10.1016/j.eswa.2010.02.016
   Google Scholar
• Li, X., Zhao, H., & Zhu, W. (2015). A cost sensitive decision tree algorithm with two adaptive mechanisms. Knowledge-Based Systems, 88, 24–33. doi:10.1016/j.knosys.2015.08.012
   Web of Science ®Google Scholar
• Li, Z., Tian, Y., Li, K., Zhou, F., & Yang, W. (2017). Reject inference in credit scoring using Semi-supervised Support Vector Machines. Expert Systems with Applications, 74, 105–114. doi:10.1016/j.eswa.2017.01.011
   Google Scholar
• Liang, J., Zhao, Y., & Zhang, X. (2016). Utility indifference valuation of corporate bond with credit rating migration by structure approach. Economic Modelling, 54, 339–346. doi:10.1016/j.econmod.2015.12.002
   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. doi:10.1057/palgrave.jors.2601976
   Web of Science ®Google Scholar
• López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013). An insight into classification with imbalanced data : Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113–141. doi:10.1016/j.ins.2013.07.007
   Web of Science ®Google Scholar
• Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering Applications of Artificial Intelligence, 65, 465–470. doi:10.1016/j.engappai.2016.12.002
   Web of Science ®Google Scholar
• Mahmoudi, N., Docherty, P., & Moscato, P. (2018). Deep neural networks understand investors better. Decision Support Systems, 112, 23–34. doi:10.1016/j.dss.2018.06.002
   Web of Science ®Google Scholar
• Mai, F., Tian, S., Lee, C., & Ma, L. (2018). Deep learning models for bankruptcy prediction using textual disclosures. European Journal of Operational Research, (xxxx). doi:10.1016/j.ejor.2018.10.024
   Google Scholar
• Maldonado, S., Pérez, J., & Bravo, C. (2017). Cost-based feature selection for Support Vector Machines: An application in credit scoring. European Journal of Operational Research, 261(2), 656–665. doi:10.1016/j.ejor.2017.02.037
   Google Scholar
• Marais, M. L., Patel, J., & Wolfson, M. (2006). The Experimental Design of Classification Models: An Application of Recursive Partitioning and Bootstrapping to Commercial Bank Loan Classifications. Journal of Accounting Research, 22, 87–114. doi:10.2307/2490861
   Google Scholar
• Marqués, A. I., García, V., & Sánchez, J. S. (2012). Two-level classifier ensembles for credit risk assessment. Expert Systems with Applications, 39(12), 10916–10922. doi:10.1016/j.eswa.2012.03.033
   Web of Science ®Google Scholar
• Martens, D., Van Gestel, T., De Backer, M., Haesen, R., Vanthienen, J., & Baesens, B. (2010). Credit rating prediction using Ant Colony Optimization. Journal of the Operational Research Society, 61(4), 561–573. doi:10.1057/jors.2008.164
   Web of Science ®Google Scholar
• Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018). Classification using Deep Learning Neural Networks for Brain Tumors. Future Computing and Informatics Journal, 3(1), 68–71. doi:10.1016/j.fcij.2017.12.001
   Google Scholar
• Nikolic, N., Zarkic-Joksimovic, N., Stojanovski, D., & Joksimovic, I. (2013). The application of brute force logistic regression to corporate credit scoring models : Evidence from Serbian financial statements. Expert Systems with Applications, 40(15), 5932–5944. doi:10.1016/j.eswa.2013.05.022
   Web of Science ®Google Scholar
• Öcsüt, H., Docsanay, M. M., Ceylan, N. B., & Aktaş, R. (2012). Prediction of bank financial strength ratings: The case of Turkey. Economic Modelling, 29(3), 632–640. doi:10.1016/j.econmod.2012.01.010
   Web of Science ®Google Scholar
• Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464–473. doi:10.1016/j.dss.2011.10.007
   Web of Science ®Google Scholar
• Ozturk, H., Namli, E., & Erdal, H. I. (2016). Modelling sovereign credit ratings: The accuracy of models in a heterogeneous sample. Economic Modelling, 54, 469–478. doi:10.1016/j.econmod.2016.01.012
   Web of Science ®Google Scholar
• Pinches, G. E., & Mingo, K. A. (1973). A Multivariate Analysis of Industrial Bond Ratings. The Journal of Finance, 28(1), 1–18. doi:10.2307/2978164
   Web of Science ®Google Scholar
• Qawaqneh, Z., Abu, A., & Barkana, B. D. (2017). Age and gender classification from speech and face images by jointly fine-tuned deep neural networks. Expert Systems with Applications, 85, 76–86. doi:10.1016/j.eswa.2017.05.037
   Web of Science ®Google Scholar
• Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017). Medical image retrieval using deep convolutional neural network. Neurocomputing, 266, 8–20. doi:10.1016/j.neucom.2017.05.025
   Web of Science ®Google Scholar
• Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. doi:10.1023/A:1022643204877
   Google Scholar
• Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Series in Machine Learning.
   Google Scholar
• Ravi Kumar, P., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review. European Journal of Operational Research, 180(1), 1–28. doi:10.1016/j.ejor.2006.08.043
   Web of Science ®Google Scholar
• Reusens, P., & Croux, C. (2017). Sovereign credit rating determinants: A comparison b efore and after the European debt crisis. Journal of Banking and Finance, 77, 108–121. doi:10.1016/j.jbankfin.2017.01.006
   Web of Science ®Google Scholar
• Sahin, Y., Bulkan, S., & Duman, E. (2013). A cost-sensitive decision tree approach for fraud detection. Expert Systems with Applications, 40(15), 5916–5923. doi:10.1016/j.eswa.2013.05.021
   Web of Science ®Google Scholar
• Salakhutdinov, R., & Hinton, G. (2009). Deep Boltzmann Machines. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. CVPR Workshops, 2009(2), 2735–2742.
   Google Scholar
• Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227. doi:10.1007/BF00116037
   Web of Science ®Google Scholar
• Serrano-Cinca, C., & Gutiérrez-Nieto, B. (2013). Partial least square discriminant analysis for bankruptcy prediction. Decision Support Systems, 54(3), 1245–1255. doi:10.1016/j.dss.2012.11.015
   Web of Science ®Google Scholar
• Sharan, R. V., & Moir, T. J. (2017). Robust acoustic event classification using deep neural networks. Information Sciences, 396, 24–32. doi:10.1016/j.ins.2017.02.013
   Web of Science ®Google Scholar
• Sheng, S., & Ling, C. X. (2005). Hybrid cost-sensitive decision tree. In Jorge A.M., Torgo L., Brazdil P., Camacho R., Gama J. (Eds). European Conference on Principles of Data Mining and Knowledge Discovery (pp. 274–284), Berlin: Springer.
   Google Scholar
• Siers, M. J., & Islam, Z. (2015). Software defect prediction using a cost sensitive decision forest and voting, and a potential solution to the class imbalance problem. Information Systems, 51, 62–71. doi:10.1016/j.is.2015.02.006
   Web of Science ®Google Scholar
• Siers, M. J., & Islam, Z. (2018). Novel algorithms for cost-sensitive classification and knowledge discovery in class imbalanced datasets with an application to NASA software defects. Information Sciences, 459, 53–70. doi:10.1016/j.ins.2018.05.035
   Web of Science ®Google Scholar
• Skurichina, M., & Duin, R. P. W. (2002). Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications, 5(2), 121–135. doi:10.1007/s100440200011
   Google Scholar
• Sohn, S. Y., Kim, D. H., & Yoon, J. H. (2016). Technology credit scoring model with fuzzy logistic regression. Applied Soft Computing, 43, 150–158. doi:10.1016/j.asoc.2016.02.025
   Web of Science ®Google Scholar
• Sun, J., Jia, M., & Li, H. (2011). AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies. Expert Systems with Applications, 38(8), 9305–9312. doi:10.1016/j.eswa.2011.01.042
   Web of Science ®Google Scholar
• Sun, Y., Kamel, M. S., Wong, A. K. C., & Wang, Y. (2007). Cost-sensitive boosting for classification of imbalanced data. 40, 3358–3378. doi:10.1016/j.patcog.2007.04.009
   Google Scholar
• Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 115, 124–135. doi:10.1016/j.ress.2013.02.022
   Web of Science ®Google Scholar
• Ting, K. M. (2002). An instance-weighting method to induce cost-sensitive decision trees. IEEE Transactions on Knowledge and Data Engineering, 14(3), 659–665.
   Web of Science ®Google Scholar
• Tomczak, J. M., & Zięba, M. (2015). Classification restricted Boltzmann machine for comprehensible credit scoring model. Expert Systems with Applications, 42(4), 1789–1796. doi:10.1016/j.eswa.2014.10.016
   Web of Science ®Google Scholar
• Tran, V., Alsakka, R., & Gwilym, O. (2014). Sovereign rating actions and the implied volatility of stock index options. International Review of Financial Analysis, 34, 101–113. doi:10.1016/j.irfa.2014.05.010
   Web of Science ®Google Scholar
• Tsang, I. W., Kwok, J. T., & Cheung, P.-M. (2005). Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research, 6, 363–392.
   Web of Science ®Google Scholar
• Tseng, F. M., & Hu, Y. C. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846–1853. doi:10.1016/j.eswa.2009.07.081
   Web of Science ®Google Scholar
• Veganzones, D., & Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111–124. (July), doi:10.1016/j.dss.2018.06.011
   Web of Science ®Google Scholar
• Villuendas-Rey, Y., Rey-Benguría, C. F., Ferreira-Santiago, Á., Camacho-Nieto, O., & Yáñez-Márquez, C. (2017). The Naïve Associative Classifier (NAC): A novel, simple, transparent, and accurate classification model evaluated on financial data. Neurocomputing, 265, 105–115. doi:10.1016/j.neucom.2017.03.085
   Web of Science ®Google Scholar
• Wang, G., Hao, J., Ma, J., & Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 38(1), 223–230. doi:10.1016/j.eswa.2010.06.048
   Web of Science ®Google Scholar
• Wang, L., Yang, Y., Min, R., & Chakradhar, S. (2017). Accelerating deep neural network training with inconsistent stochastic gradient descent. Neural Networks, 93, 219–229. doi:10.1016/j.neunet.2017.06.003
   PubMed Web of Science ®Google Scholar
• Wang, T., Qin, Z., Zhang, S., & Zhang, C. (2012). Cost-sensitive classification with inadequate labeled data. Information Systems, 37(5), 508–516. doi:10.1016/j.is.2011.10.009
   Web of Science ®Google Scholar
• Wang, Z., Jiang, C., Ding, Y., Lyu, X., & Liu, Y. (2018). A novel behavioral scoring model for estimating probability of default over time in peer-to-peer lending. Electronic Commerce Research and Applications, 27, 74–82. doi:10.1016/j.elerap.2017.12.006
   Web of Science ®Google Scholar
• Witten, I. H., Frank, I., Hall, M. A., & Pal, C. (2016). Data mining. practical machine learning tools and techniques. San Francisco: Morgan Kaufmann.
   Google Scholar
• Xia, Y., Liu, C., Da, B., & Xie, F. (2018). A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Systems with Applications, 93, 182–199. doi:10.1016/j.eswa.2017.10.022
   Web of Science ®Google Scholar
• Xia, Y., Liu, C., Li, Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Systems With Applications, 78, 225–241. doi:10.1016/j.eswa.2017.02.017
   Google Scholar
• Xiao, H., Xiao, Z., & Wang, Y. (2016). Ensemble classification based on supervised clustering for credit scoring. Applied Soft Computing Journal, 43, 73–86. doi:10.1016/j.asoc.2016.02.022
   Web of Science ®Google Scholar
• Yang, H., Ahn, H. J., Kim, M. H., & Ryu, D. (2017). Information asymmetry and investor trading behavior around bond rating change announcements. Emerging Markets Review, 32, 38–51. doi:10.1016/j.ememar.2017.05.004
   Google Scholar
• Yang, Z., You, W., & Ji, G. (2011). Using partial least squares and support vector machines for bankruptcy prediction. Expert Systems with Applications, 38(7), 8336–8342. doi:10.1016/j.eswa.2011.01.021
   Google Scholar
• Zhang, Q., Wang, J., Lu, A., Wang, S., & Ma, J. (2018). An improved SMO algorithm for financial credit risk assessment – evidence from China’s banking. Neurocomputing, 272, 314–325. doi:10.1016/j.neucom.2017.07.002
   Web of Science ®Google Scholar
• Zhou, L., Lu, D., & Fujita, H. (2015). The performance of corporate financial distress prediction models with features selection guided by domain knowledge and data mining approaches. Knowledge-Based Systems, 85, 52–61. doi:10.1016/j.knosys.2015.04.017
   Web of Science ®Google Scholar
• Zhou, Z. H., & Liu, X. Y. (2006). Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. IEEE Transactions on Knowledge and Data Engineering, 18(1), 63–77.
   Web of Science ®Google Scholar
• Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. doi:10.2307/2490859
   Web of Science ®Google Scholar