A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method
International Journal of Information and Communication Sciences
Volume 4, Issue 3, September 2019, Pages: 52-58
Received: Aug. 5, 2019;
Published: Sep. 27, 2019
Views 851 Downloads 197
Xiaoxia Wu, Department of Financial Accounting, Zhejiang Institute of Economics and Trade, Hangzhou, China
Dongqi Yang, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Shuai Zhang, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).
A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method, International Journal of Information and Communication Sciences.
Vol. 4, No. 3,
2019, pp. 52-58.
Friedman, J., Hastie, T., & Tibshirani, R., “Additive logistic regression: a statistical view of boosting.” The Annals of Statistics, vol. 28, no. 2, 2000, pp. 400-407.
John, G. H., & Langley, P., “Estimating continuous distributions in bayesian classifiers.” In Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Montreal, Canada, Aug 18-20, 1995, pp. 338-345.
Cessie, S. L., & Houwelingen, J. C. V., “Ridge estimators in logistic regression.” Journal of the Royal Statistical Society, vol. 41, no. 1, 1992, pp. 191-201.
Back, B., Laitinen, T., & Sere, K., “Neural networks and genetic algorithms for bankruptcy predictions.” Expert Systems with Applications, vol. 11, no. 4, 1996, pp. 407-413.
Quinlan, J. R., “C4.5: programs for machine learning.” Morgan Kaufmann Publishers Inc., 1993.
Breiman, L., “Random forests.” Machine Learning, vol. 45, no. 1, 2001, pp. 5-32.
Freund, Y., & Schapire, R. E., “Experiments with a new boosting algorithm.” In Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, July 3-6, 1996, pp. 148-156.
Breiman, L., “Bagging predictors.” Machine Learning, vol. 24, no. 2, 1996, pp. 123-140.
Aha, D. W., Kibler, D., & Albert, M. K., “Instance-based learning algorithms.” Machine Learning, vol. 6, no. 1, 1991, pp. 37-66.
Freund, Y., & Schapire, R. E., “Large margin classification using the perceptron algorithm.” Machine Learning, vol. 37, no. 3, 1999, pp. 277-296.
Foster, D. P., & Stine, R. A., “Variable selection in data mining: building a predictive model for bankruptcy.” Journal of the American Statistical Association, vol. 99, no. 466, 2004, pp. 303-313.
Alfaro, E., & Elizondo, D., “Bankruptcy forecasting: an empirical comparison of adaboost and neural networks.” Decision Support Systems, vol. 45, no. 1, 2008, pp. 110-122.
Nanni, L., & Lumini, A., “An experimental comparison of ensemble classifiers for bankruptcy prediction and credit scoring.” Expert Systems with Applications, vol. 36, no. 2, 2009, pp. 3028-3033.
Li, M. Y. L., & Miu, P., “A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: a binary quantile regression approach.” Journal of Empirical Finance, vol. 17, no. 4, 2010, pp. 818-833.
Charitou, A., Dionysiou, D., Lambertides, N., & Trigeorgis, L., “Alternative bankruptcy prediction models using option-pricing theory.” Journal of Banking & Finance, vol. 37, no. 4, 2011, pp. 2329-2341.
Wang, G., Ma, J., & Yang, S., “An improved boosting based on feature selection for corporate bankruptcy prediction.” Expert Systems with Applications, vol. 41, no. 5, 2014, pp. 2353-2361.
Zieba, M., Tomczak, S. K., & Tomczak, J. M., “Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction.” Expert Systems with Applications, vol. 58, no. C, 2016, pp. 93-101.
Kim, H. J., Jo, N. O., & Shin, K. S., “Optimization of cluster-based evolutionary undersampling for the artificial neural networks in corporate bankruptcy prediction.” Expert Systems with Applications, vol. 59, 2016, pp. 226-234.
Barboza, F., Kimura, H., & Altman, E., “Machine learning models and bankruptcy prediction.” Expert Systems with Applications, vol. 83, 2017, pp. 405-417.
Jardin, P. D., “Dynamics of firm financial evolution and bankruptcy prediction.” Expert Systems with Applications, vol. 75, 2017, pp. 25-43.
Wang, M., Chen, H., Li, H., Cai, Z., Zhao, X., & Tong, C., et al., “Grey wolf optimization evolving kernel extreme learning machine: application to bankruptcy prediction.” Engineering Applications of Artificial Intelligence, vol. 63, 2017, pp. 54-68.
Kliestik, T., Misankova, M., Valaskova, K., & Svabova, L., “Bankruptcy prevention: new effort to reflect on legal and social changes.” Science & Engineering Ethics, vol. 4, 2017, pp. 1-13.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P., “Smote: synthetic minority over-sampling technique.” Journal of Artificial Intelligence Research, vol. 16, no. 1, 2002, pp. 321-357.
Fayyad, U., “Multi-interval discretization of continuous-valued attributes for classification learning.” In Proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambery, France, Aug 28-Sept 3, 1993, pp. 1022-1027.