Corporate bankruptcy and insolvency prediction model
Abstract
In any competitive economy, the risk of bankruptcy is pervasive. The research aims to contribute in improving the predictive power of bankruptcy and insolvency risk among companies by introducing new methods of processing and validation. This paper investigates the extensive application of the Z score model for predicting the economic-financial stability of Romanian companies in the manufacturing and extractive industries. A list of 37 financial indicators determined on the basis of the balance sheet data of 80 companies for the period 2015–2018 was used. Stepwise Least Squares Estimation through the Forward method allowed the identification of the most relevant ones. Canonical discriminant analysis and sensitivity analyzes were introduced to test the predictive power of the model. The new model identified allows both the prediction of bankruptcy and insolvency risk. This study contributes to the literature by testing variables in relation to financial difficulties and by including other classification information. The robustness of the determined canonical discriminant function was verified by testing the model on two other samples.
Keyword : bankruptcy, insolvency, risk, multiple discriminant analysis, prediction model
How to Cite
Voda, A. D., Dobrotă, G., Țîrcă, D. M., Dumitrașcu, D. D., & Dobrotă, D. (2021). Corporate bankruptcy and insolvency prediction model . Technological and Economic Development of Economy, 27(5), 1039-1056. https://doi.org/10.3846/tede.2021.15106
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Altman, E. I. (1983). Corporate financial distress. Wiley Interscience.
Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy. John Wiley & Sons. https://doi.org/10.1002/9781118267806
Barniv, R., Agarwal, A., & Leach, R. (2002). Predicting bankruptcy resolution. Journal of Business Finance & Accounting, 29(3–4), 497–520. https://doi.org/10.1111/1468-5957.00440
Bauweraerts, J. (2016). Predicting bankruptcy in private firms: Towards a stepwise regression procedure. International Journal of Financial Research, 7(2), 147–153. https://doi.org/10.5430/ijfr.v7n2p147
Beaver, W. H. (1966). Financial ratios as prediction of failure. Journal of Accounting Research, 3(71), 150–161. https://doi.org/10.2307/2490171
Beaver, W. H., McNichols, M. F., & Rhie, J. W. (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.
Beynon, M., & Peel, M. (2001). Variable precision rough set theory and data discretization: An application to corporate failure prediction. Omega, 29(6), 561–576. https://doi.org/10.1016/S0305-0483(01)00045-7
Bernard, A., Redding, S., & Schott, P. (2007). Comparative advantage and heterogeneous firms. Review of Economic Studies, 74(1), 31–66. https://doi.org/10.1111/j.1467-937X.2007.00413.x
Boďa, M., & Úradníček, V. (2016). The portability of altman’s Z-score model to predicting corporate financial distress of Slovak companies. Technological and Economic Development of Economy, 22(4), 532–553. https://doi.org/10.3846/20294913.2016.1197165
Chen, Z., Chen, W., & Shi, Y. (2020). Ensemble learning with label proportions for bankruptcy prediction. Expert Systems with Applications, 146, 113–155. https://doi.org/10.1016/j.eswa.2019.113155
Conan, J., & Holder, M. (1979). Variables explicatives de performances et contrôle de gestion dans les PMI [Thèse de Doctorat en sciences de gestion]. Université de Paris IX, Paris.
Deakin, E. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10, 167–179. https://doi.org/10.2307/2490225
du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286–303. https://doi.org/10.1016/j.ejor.2014.09.059
Ékes, K. S., & Koloszár, L. (2014). The efficiency of bankruptcy forecast models in the Hungarian SME Sector. Journal of Competitiveness, 6(2), 56–73. https://doi.org/10.7441/joc.2014.02.05
Garškienė, A., & Garškaitė, K. (2004). Enterprise bankruptcy in Lithuania. Journal of Business Economics and Management, 5(1), 51–58. https://doi.org/10.3846/16111699.2004.9636068
Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156–1173. https://doi.org/10.3846/16111699.2017.1400461
Giacosa, E., Halili, E., Mazzoleni, A., Teodori, C., & Veneziani, M. (2016). Re-estimation of company insolvency prediction models: survey on Italian manufacturing companies. Corporate Ownership and Control, 14(1), 159–174. https://doi.org/10.22495/cocv14i1c1p1
Grice, J. S., & Ingram, R. W. (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54(1), 53–61. https://doi.org/10.1016/S0148-2963(00)00126-0
Hsieh, S. J. (1993). A note on the optimal cutoff point in bankruptcy prediction models. Journal of Business Finance & Accounting, 20(3), 457–464. https://doi.org/10.1111/j.1468-5957.1993.tb00268.x
Hwang, R. C., Cheng, K. F., & Lee, C. F. (2009). On multiple-class prediction of issuer credit ratings. Applied Stochastic Models in Business and Industry, 25(5), 535–550. https://doi.org/10.1002/asmb.735
Jabeur, S. B. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services, 36, 197–202. https://doi.org/10.1016/j.jretconser.2017.02.005
Jackson, R. H. G., & Wood, A. (2013). The performance of insolvency prediction and credit risk models in the UK: A comparative study. The British Accounting Review, 45(3), 183–202. https://doi.org/10.1016/j.bar.2013.06.009
Karas, M., Reznakova, M., Bartos, V., & Zinecker, M. (2013). Possibilities for the application of the Altman model within the Czech Republic. In Recent Researches in Law Science and Finances (pp. 203–207). http://www.wseas.us/e-library/conferences/2013/Chania/ICFA/ICFA-30.pdf
Kliestik, T., Vrbka, J., & Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3), 569–593. https://doi.org/10.24136/eq.2018.028
Ko, Y. C., Fujita, H., & Li, T. (2017). An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Applied Soft Computing, 52, 748–759. https://doi.org/10.1016/j.asoc.2016.09.050
Kostrzewska, J., Kostrzewski, M., Pawelek, B., & Galuszka, K. (2016). The classical and Bayesian logistic regression in the research on the financial standing of enterprises after bankruptcy in Poland. In Proceedings of the 10th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena (pp. 72–81). Foundation of the Cracow University of Economics.
Lachenbruch, P. A. (1967). An almost unbiased method of obtaining confidence levels for the probability of misclassification in discriminant analysis. Biometrics, 23, 639–645. https://doi.org/10.2307/2528418
Levratto, N. (2013). From failure to corporate bankruptcy: a review. Journal of Innovation and Entrepreneurship, 2, 20. https://doi.org/10.1186/2192-5372-2-20
Memić, D. (2015). Assessing credit default using logistic regression and multiple discriminant analysis: Empirical evidence from Bosnia and Herzegovina. Interdisciplinary Description of Complex Systems: INDECS, 13(1), 128–153. https://doi.org/10.7906/indecs.13.1.13
Neumaier, I., & Neumaierová, I. (2004). Index IN 05. In P. Červinek (Ed.), Sborník příspěvků mezinárodní vědecké konference „Evropské fi nanční systémy“ (pp. 143–148). Ekonomickosprávní fakulta Masarykovy University.
Noga, T., & Adamowicz, K. (2021). Forecasting bankruptcy in the wood industry. European Journal of Wood Products, 79, 735–743. https://doi.org/10.1007/s00107-020-01620-y
Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. Proceedings of the International Joint Conference on Neural Networks, 2, 163–167. https://doi.org/10.1109/IJCNN.1990.137710
Ohlson, J. (1980). Financial ratio and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
Pan, W. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74. https://doi.org/10.1016/j.knosys.2011.07.001
Peyman, I., Mehdi, M.-J., & Petro, S. (2011). A study of the application of springate and zmijewski bankruptcy prediction models in firms accepted in Tehran Stock Exchange. Australian Journal of Basic and Applied Sciences, 5(11), 1546–1550.
Philosophov, L., & Philosophov, V. (2002). Corporate bankruptcy prognosis: An attempt at a combined prediction of the bankruptcy event and time interval of its occurrence. International Review of Financial Analysis, 11(3), 375–406. https://doi.org/10.1016/S1057-5219(02)00081-9
Rodrigues, L., & Rodrigues, L. (2018). Economic-financial performance of the Brazilian sugarcane energy industry: An empirical evaluation using financial ratio, cluster and discriminant analysis. Biomass and Bioenergy, 108, 289–296. https://doi.org/10.1016/j.biombioe.2017.11.013
Ruxanda, G., Zamfir, C., & Muraru, A. (2018). Predicting financial distress for Romanian companies. Technological and Economic Development Economy, 24(6), 2318–2337. https://doi.org/10.3846/tede.2018.6736
Salehi, M., & Pour, M. D. (2016). Bankruptcy prediction of listed companies on the Tehran Stock Exchange. International Journal of Law and Management, 58(5), 545–561. https://doi.org/10.1108/IJLMA-05-2015-0023
Salehi, M., & Shri, M. M. (2016). Different bankruptcy prediction patterns in an emerging economy: Iranian evidence. International Journal of Law and Management, 58(3), 258–280. https://doi.org/10.1108/IJLMA-05-2015-0022
Salehi, M., Shri, M. M., & Bolandraftar, M. (2016). Predicting corporate financial distress using data mining techniques: An application in Tehran Stock Exchange. International Journal of Law and Management, 58(2), 216–230. https://doi.org/10.1108/IJLMA-06-2015-0028
Schielke, H. J., Fishman, J. L., Osatuke, K., & Stiles, W. B. (2009). Creative consensus on interpretations of qualitative data: The Ward method. Psychotherapy Research, 19(4–5), 558–565. https://doi.org/10.1080/10503300802621180
Smiti, S., & Soui, M. (2020). Bankruptcy prediction using deep learning approach based on Borderline SMOTE. Information Systems Frontiers, 5(22), 1067–1083. https://doi.org/10.1007/s10796-020-10031-6
Sulub, S. A. (2014). Testing the predictive power of Altman’s revised Z’ model: the case of 10 multinational companies. Research Journal of Finance and Accounting, 5(21), 174–184.
Taffler, R. J. (1982). Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of the Royal Statistical Society: Series A (General), 145(3), 342–358. https://doi.org/10.2307/2981867
Taffler, R. J. (1983, March 22–24). The Z-Score approach to measuring company solvency. The Accountant’s Magazine.
Takahashi, K., Kurokawa, Y., & Watase, K. (1984). Corporate bankruptcy prediction in Japan. Journal of Banking & Finance, 8(2), 229–247. https://doi.org/10.1016/0378-4266(84)90005-0
Telipenko, E. V., Zakharova, A. A., & Sopova, S. P. (2015). Forecasting risk of bankruptcy for machine-building plants. IOP Conference Series: Materials Science and Engineering, 19(1), 012066. https://doi.org/10.1088/1757-899X/91/1/012066
Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419. https://doi.org/10.1016/j.irfa.2013.02.013
Valaskova, K., Kliestik, T., Svabova, L., & Adamko, P. (2018). Financial risk measurement and prediction modelling for sustainable development of business entities using regresision analysis. Sustainability, 10(7), 2144. https://doi.org/10.3390/su10072144
Veganzones, D., & Severin, E. (2020). Corporate failure prediction models in the twenty-first century: a review. European Business Review, 33(2), 204–226. https://doi.org/10.1108/EBR-12-2018-0209
Wang, L., & Wu, C. (2017). Business failure prediction based on two-stage selective ensemble with manifold learning algorithm and kernel-based fuzzy self-organizing map. Knowledge-Based Systems, 121, 99–110. https://doi.org/10.1016/j.knosys.2017.01.016
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 4(23), 589–609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I. (1983). Corporate financial distress. Wiley Interscience.
Altman, E. I., & Hotchkiss, E. (2006). Corporate financial distress and bankruptcy. John Wiley & Sons. https://doi.org/10.1002/9781118267806
Barniv, R., Agarwal, A., & Leach, R. (2002). Predicting bankruptcy resolution. Journal of Business Finance & Accounting, 29(3–4), 497–520. https://doi.org/10.1111/1468-5957.00440
Bauweraerts, J. (2016). Predicting bankruptcy in private firms: Towards a stepwise regression procedure. International Journal of Financial Research, 7(2), 147–153. https://doi.org/10.5430/ijfr.v7n2p147
Beaver, W. H. (1966). Financial ratios as prediction of failure. Journal of Accounting Research, 3(71), 150–161. https://doi.org/10.2307/2490171
Beaver, W. H., McNichols, M. F., & Rhie, J. W. (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.
Beynon, M., & Peel, M. (2001). Variable precision rough set theory and data discretization: An application to corporate failure prediction. Omega, 29(6), 561–576. https://doi.org/10.1016/S0305-0483(01)00045-7
Bernard, A., Redding, S., & Schott, P. (2007). Comparative advantage and heterogeneous firms. Review of Economic Studies, 74(1), 31–66. https://doi.org/10.1111/j.1467-937X.2007.00413.x
Boďa, M., & Úradníček, V. (2016). The portability of altman’s Z-score model to predicting corporate financial distress of Slovak companies. Technological and Economic Development of Economy, 22(4), 532–553. https://doi.org/10.3846/20294913.2016.1197165
Chen, Z., Chen, W., & Shi, Y. (2020). Ensemble learning with label proportions for bankruptcy prediction. Expert Systems with Applications, 146, 113–155. https://doi.org/10.1016/j.eswa.2019.113155
Conan, J., & Holder, M. (1979). Variables explicatives de performances et contrôle de gestion dans les PMI [Thèse de Doctorat en sciences de gestion]. Université de Paris IX, Paris.
Deakin, E. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10, 167–179. https://doi.org/10.2307/2490225
du Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286–303. https://doi.org/10.1016/j.ejor.2014.09.059
Ékes, K. S., & Koloszár, L. (2014). The efficiency of bankruptcy forecast models in the Hungarian SME Sector. Journal of Competitiveness, 6(2), 56–73. https://doi.org/10.7441/joc.2014.02.05
Garškienė, A., & Garškaitė, K. (2004). Enterprise bankruptcy in Lithuania. Journal of Business Economics and Management, 5(1), 51–58. https://doi.org/10.3846/16111699.2004.9636068
Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156–1173. https://doi.org/10.3846/16111699.2017.1400461
Giacosa, E., Halili, E., Mazzoleni, A., Teodori, C., & Veneziani, M. (2016). Re-estimation of company insolvency prediction models: survey on Italian manufacturing companies. Corporate Ownership and Control, 14(1), 159–174. https://doi.org/10.22495/cocv14i1c1p1
Grice, J. S., & Ingram, R. W. (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54(1), 53–61. https://doi.org/10.1016/S0148-2963(00)00126-0
Hsieh, S. J. (1993). A note on the optimal cutoff point in bankruptcy prediction models. Journal of Business Finance & Accounting, 20(3), 457–464. https://doi.org/10.1111/j.1468-5957.1993.tb00268.x
Hwang, R. C., Cheng, K. F., & Lee, C. F. (2009). On multiple-class prediction of issuer credit ratings. Applied Stochastic Models in Business and Industry, 25(5), 535–550. https://doi.org/10.1002/asmb.735
Jabeur, S. B. (2017). Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services, 36, 197–202. https://doi.org/10.1016/j.jretconser.2017.02.005
Jackson, R. H. G., & Wood, A. (2013). The performance of insolvency prediction and credit risk models in the UK: A comparative study. The British Accounting Review, 45(3), 183–202. https://doi.org/10.1016/j.bar.2013.06.009
Karas, M., Reznakova, M., Bartos, V., & Zinecker, M. (2013). Possibilities for the application of the Altman model within the Czech Republic. In Recent Researches in Law Science and Finances (pp. 203–207). http://www.wseas.us/e-library/conferences/2013/Chania/ICFA/ICFA-30.pdf
Kliestik, T., Vrbka, J., & Rowland, Z. (2018). Bankruptcy prediction in Visegrad group countries using multiple discriminant analysis. Equilibrium. Quarterly Journal of Economics and Economic Policy, 13(3), 569–593. https://doi.org/10.24136/eq.2018.028
Ko, Y. C., Fujita, H., & Li, T. (2017). An evidential analysis of Altman Z-score for financial predictions: Case study on solar energy companies. Applied Soft Computing, 52, 748–759. https://doi.org/10.1016/j.asoc.2016.09.050
Kostrzewska, J., Kostrzewski, M., Pawelek, B., & Galuszka, K. (2016). The classical and Bayesian logistic regression in the research on the financial standing of enterprises after bankruptcy in Poland. In Proceedings of the 10th professor Aleksander Zelias international conference on modelling and forecasting of socio-economic phenomena (pp. 72–81). Foundation of the Cracow University of Economics.
Lachenbruch, P. A. (1967). An almost unbiased method of obtaining confidence levels for the probability of misclassification in discriminant analysis. Biometrics, 23, 639–645. https://doi.org/10.2307/2528418
Levratto, N. (2013). From failure to corporate bankruptcy: a review. Journal of Innovation and Entrepreneurship, 2, 20. https://doi.org/10.1186/2192-5372-2-20
Memić, D. (2015). Assessing credit default using logistic regression and multiple discriminant analysis: Empirical evidence from Bosnia and Herzegovina. Interdisciplinary Description of Complex Systems: INDECS, 13(1), 128–153. https://doi.org/10.7906/indecs.13.1.13
Neumaier, I., & Neumaierová, I. (2004). Index IN 05. In P. Červinek (Ed.), Sborník příspěvků mezinárodní vědecké konference „Evropské fi nanční systémy“ (pp. 143–148). Ekonomickosprávní fakulta Masarykovy University.
Noga, T., & Adamowicz, K. (2021). Forecasting bankruptcy in the wood industry. European Journal of Wood Products, 79, 735–743. https://doi.org/10.1007/s00107-020-01620-y
Odom, M. D., & Sharda, R. (1990). A neural network model for bankruptcy prediction. Proceedings of the International Joint Conference on Neural Networks, 2, 163–167. https://doi.org/10.1109/IJCNN.1990.137710
Ohlson, J. (1980). Financial ratio and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
Pan, W. (2012). A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Knowledge-Based Systems, 26, 69–74. https://doi.org/10.1016/j.knosys.2011.07.001
Peyman, I., Mehdi, M.-J., & Petro, S. (2011). A study of the application of springate and zmijewski bankruptcy prediction models in firms accepted in Tehran Stock Exchange. Australian Journal of Basic and Applied Sciences, 5(11), 1546–1550.
Philosophov, L., & Philosophov, V. (2002). Corporate bankruptcy prognosis: An attempt at a combined prediction of the bankruptcy event and time interval of its occurrence. International Review of Financial Analysis, 11(3), 375–406. https://doi.org/10.1016/S1057-5219(02)00081-9
Rodrigues, L., & Rodrigues, L. (2018). Economic-financial performance of the Brazilian sugarcane energy industry: An empirical evaluation using financial ratio, cluster and discriminant analysis. Biomass and Bioenergy, 108, 289–296. https://doi.org/10.1016/j.biombioe.2017.11.013
Ruxanda, G., Zamfir, C., & Muraru, A. (2018). Predicting financial distress for Romanian companies. Technological and Economic Development Economy, 24(6), 2318–2337. https://doi.org/10.3846/tede.2018.6736
Salehi, M., & Pour, M. D. (2016). Bankruptcy prediction of listed companies on the Tehran Stock Exchange. International Journal of Law and Management, 58(5), 545–561. https://doi.org/10.1108/IJLMA-05-2015-0023
Salehi, M., & Shri, M. M. (2016). Different bankruptcy prediction patterns in an emerging economy: Iranian evidence. International Journal of Law and Management, 58(3), 258–280. https://doi.org/10.1108/IJLMA-05-2015-0022
Salehi, M., Shri, M. M., & Bolandraftar, M. (2016). Predicting corporate financial distress using data mining techniques: An application in Tehran Stock Exchange. International Journal of Law and Management, 58(2), 216–230. https://doi.org/10.1108/IJLMA-06-2015-0028
Schielke, H. J., Fishman, J. L., Osatuke, K., & Stiles, W. B. (2009). Creative consensus on interpretations of qualitative data: The Ward method. Psychotherapy Research, 19(4–5), 558–565. https://doi.org/10.1080/10503300802621180
Smiti, S., & Soui, M. (2020). Bankruptcy prediction using deep learning approach based on Borderline SMOTE. Information Systems Frontiers, 5(22), 1067–1083. https://doi.org/10.1007/s10796-020-10031-6
Sulub, S. A. (2014). Testing the predictive power of Altman’s revised Z’ model: the case of 10 multinational companies. Research Journal of Finance and Accounting, 5(21), 174–184.
Taffler, R. J. (1982). Forecasting company failure in the UK using discriminant analysis and financial ratio data. Journal of the Royal Statistical Society: Series A (General), 145(3), 342–358. https://doi.org/10.2307/2981867
Taffler, R. J. (1983, March 22–24). The Z-Score approach to measuring company solvency. The Accountant’s Magazine.
Takahashi, K., Kurokawa, Y., & Watase, K. (1984). Corporate bankruptcy prediction in Japan. Journal of Banking & Finance, 8(2), 229–247. https://doi.org/10.1016/0378-4266(84)90005-0
Telipenko, E. V., Zakharova, A. A., & Sopova, S. P. (2015). Forecasting risk of bankruptcy for machine-building plants. IOP Conference Series: Materials Science and Engineering, 19(1), 012066. https://doi.org/10.1088/1757-899X/91/1/012066
Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419. https://doi.org/10.1016/j.irfa.2013.02.013
Valaskova, K., Kliestik, T., Svabova, L., & Adamko, P. (2018). Financial risk measurement and prediction modelling for sustainable development of business entities using regresision analysis. Sustainability, 10(7), 2144. https://doi.org/10.3390/su10072144
Veganzones, D., & Severin, E. (2020). Corporate failure prediction models in the twenty-first century: a review. European Business Review, 33(2), 204–226. https://doi.org/10.1108/EBR-12-2018-0209
Wang, L., & Wu, C. (2017). Business failure prediction based on two-stage selective ensemble with manifold learning algorithm and kernel-based fuzzy self-organizing map. Knowledge-Based Systems, 121, 99–110. https://doi.org/10.1016/j.knosys.2017.01.016