Classification Restricted Boltzmann Machine for comprehensible credit scoring model

Expert Systems with Applications

Volumn 42, Issue 4, 2015, Pages 1789-1796

  Tomczak, Jakub M ^a   Zie¸ba, Maciej ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Comprehensible model; Credit scoring; Imbalanced data; Restricted Boltzmann Machine

Indexed keywords

EXPERT SYSTEMS;

BINARY FEATURES; CREDIT SCORING; CREDIT SCORING MODEL; CUTTING POINT; GEOMETRIC MEAN; IMBALANCED DATA; RESTRICTED BOLTZMANN MACHINE; SCORING MODELS;

RISK ASSESSMENT;

EID: 84910615991     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.10.016     Document Type: Article

References (24)

1. Bellotti, T., & Crook, J. (2009). Support vector machines for credit scoring and discovery of significant features. Expert Systems with Applications, 36, 3302-3308.
2. Crook, J. N., Edelman, D. B., & Thomas, L. C. (2007). Recent developments in consumer credit risk assessment. European Journal of Operational Research, 183, 1447-1465.
3. De Bock, K. W., & Van den Poel, D. (2012). Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models. Expert Systems with Applications, 39, 6816-6826.
4. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1-30.
5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter, 11, 10-18.
6. Hand, D. J., & Henley, W. E. (1997). Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160, 523-541.
7. He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21, 1263-1284.
8. Hinton, G. E. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14, 1771-1800.
9. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 65-70.
10. 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, 847-856.
11. Huang, S. C. (2011). Using Gaussian process based kernel classifiers for credit rating forecasting. Expert Systems with Applications, 38, 8607-8611.
12. Huang, Y. M., Hung, C. M., & Jiau, H. C. (2006). Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem. Nonlinear Analysis: Real World Applications, 7, 720-747.
13. Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. ECML, 146-153.
14. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced training sets: One-sided selection. In ICML (pp. 179-186).
15. Larochelle, H., & Bengio, Y. (2008). Classification using discriminative restricted Boltzmann machines. In Proceedings of the 25th international conference on machine learning (pp. 536-543).
16. Larochelle, H., Mandel, M., Pascanu, R., & Bengio, Y. (2012). Learning algorithms for the classification restricted Boltzmann machine. Journal of Machine Learning Research, 13, 643-669.
17. Le Roux, N., & Bengio, Y. (2008). Representational power of restricted Boltzmann machines and deep belief networks. Neural Computation, 20, 1631-1649.
18. Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183, 1466-1476.
19. Martens, J., Chattopadhya, A., Pitassi, T., & Zemel, R. (2013). On the expressive power of restricted Boltzmann machines. In Advances in neural information processing systems 26 (pp. 2877-2885).
20. Nanni, L., & Lumini, A. (2009). An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring. Expert Systems with Applications, 36, 3028-3033.
21. Sutskever, I., Martens, J., Dahl, G., & Hinton, G. (2013). On the importance of initialization and momentum in deep learning. In International conference on machine learning.
22. West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27, 1131-1152.
23. Yang, Y. (2007). Adaptive credit scoring with kernel learning methods. European Journal of Operational Research, 183, 1521-1536.
24. Zie¸ba, M., & ͆wia¸tek, J. (2012). Ensemble classifier for solving credit scoring problems. Technological Innovation for Value Creation, 59-66.