Using Sample Selection to Improve Accuracy and Simplicity of Rules Extracted from Neural Networks for Credit Scoring Applications

International Journal of Computational Intelligence and Applications

Volumn 14, Issue 4, 2015, Pages

  Setiono, Rudy ^a   Azcarraga, Arnulfo ^b   Hayashi, Yoichi ^c  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^b DE LA SALLE UNIVERSITY   (Philippines)

^c MEIJI UNIVERSITY   (Japan)

Author keywords

Credit scoring; ensembles; feedforward neural networks; rule extraction

Indexed keywords

CLASSIFICATION (OF INFORMATION); EXTRACTION; STATISTICS;

CLASSIFICATION ACCURACY; CREDIT SCORING; ENSEMBLES; PREDICTIVE ACCURACY; RULE EXTRACTION; RULE EXTRACTION ALGORITHMS; SAMPLE SELECTION; TRAINING DATASET;

FEEDFORWARD NEURAL NETWORKS;

EID: 84951752839     PISSN: 14690268     EISSN: None     Source Type: Journal    
DOI: 10.1142/S1469026815500212     Document Type: Article

References (45)

1. P. J. L. Adeodato, A. L. Arnaud, G. C. Vasconcelos, R. C. L. V. Cunha, D. S. M. P. Monteiro, MLP ensembles improve long term prediction accuracy over single networks, Int. J. Forec. 27(3) (2011) 661-671.
2. M. Amini, J. Rezaeenour, E. Hadavandi, A neural network ensemble classier for e ective intrusion detection using fuzzy clustering, RBF networks, to appear in Int. J. Artif. Intell. Tools (2015).
3. A. Asuncion, D. J. Newman, UCI Repository of machine learning databases. Irvine, CA: School of of Information, Computer Sciences, University of California. Available at http://www.ics.uci.edu/-mlearn/MLRepository.html (2007).
4. H. Abdou, J. Pointon, A. El-Masry, Neural nets versus conventional techniques in credit scoring in Egyptian banking, Expert Syst. Appl. 35 (2008) 1275-1292.
5. B. Baesens, T. Van Gestel, S. Vianne, M. Stepanova, J. Suykens, J. Vanthienen, Benchmarking state-of-the-art classication algorithm for credit scoring, J. Oper. Res. Soc. 54 (2003) 627-635.
6. R. Battiti, First-and second-order methods for learning: Between steepest descent, Newton's method, Neural Comput. 4 (1992) 141-166.
7. S. Bhattacharyya, S. Jha, K. Tharakunnel, J. C. Westland, Data mining for credit card fraud: A comparative study, Dec. Support Syst. 50 (2011) 602-613.
8. C.-Y. Chiu, B. Verma, Relationship between data size, accuracy, diversity, clusters in neural netwrok ensembles, Int. J. Comput. Intell. Appl. 12(4) (2013) 1340005.
9. C. Cortes, M. Mohri, AUC optimization vs. error rate minimization, Adv. Neural Inf. Process. Syst. 16 (2004) 312-320.
10. J. E. Dennis Jr, R. E. Schnabel, Numerical Methods for Unconstrained Optimization, Nonlinear Equations (Prentice Hall, Englewood Cli s, NJ, 1983).
11. T. Fawcett, An introduction to ROC analysis, Pattern Recogn. Lett. 27 (2006) 861-874.
12. S. Finlay, Multiple classier architectures, their application to credit assessment, Eur. J. Oper. Res. 210 (2011) 368-378.
13. V. Garcia, A. I. Marqu-ez, J. S. S-anchez, On the use of data ltering techniques for credit risk prediction with instance-based models, Expert Syst. Appl. 39 (2012) 13267-13276.
14. L. K. Hansen, P. Salamon, Neural network ensembles, IEEE Trans. Pattern Anal. Mach. Intell. 12(10) (1990) 993-1001.
15. J. Hertz, A. Krogh, R. G. Palmer, Introduction to the Theory of Neural Computation (Addison Wesley, Redwood City, CA, 1991).
16. A. Khashman, Neural networks for credit risk evaluation: Investigation of di erent neural models, learning schemes, Expert Syst. Appl. 37 (2010) 6233-6239.
17. K. Kim, H. Ahn, A corporate credit rating model using multi-class support vector machines with an original pairwise partitioning approach, Comput. Oper. Res. 39 (2012) 1800-1811.
18. S. Lahmiri, M. Boukadoum, An ensemble system based on hybrid EGARCH-ANN with di erent distributional assumptions to predict S&P500 Intraday Volatility, Fluctuation Noise Lett. 14(1) (2015) 1550001.
19. F. Louzada, P. H. Ferreira-Silva, C. A. R. Diniz, On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data, Expert Syst. Appl. 39 (2012) 8071-8078.
20. V. Landassuri-Moreno, J. A. Bullinaria, Neural network ensembles for time series forecasting, in Proc. GECCO'09, Genetic, Evolutionary Computation Conf. 2009, pp. 1235-1242.
21. A. Moosavi, S. H. Khasteh, M. A. Bagheri, Forecasting crude oil price with ensemble neural networks based on di erent feature subsets method, Int. J. Energy Stat. 3(2) (2015) 1550008.
22. N. Nikolic, N. Zarkic-Joksimovic, D. Stojanovski, I. Joksimovic, The application of brute force logistic regression to corporate credit scoring models: Evidence from Serbian nancial statements, Expert Syst. Appl. 40 (2013) 5932-5944.
23. Z. I. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data (Kluwer Academic Publishing, Dordrecht, 1991).
24. L. Prechelt, Proben1: A set of benchmarks, benchmarking rules for neural network training algorithms, Technical Report 21/94, Fakultät für Informatik, Universität Karlsruhe, Germany.
25. R. Quinlan, C4.5: Programs for Machine Learning (Morgan Kaufmann, San Mateo, CA, 1993).
26. J. S-anchez, F. Fla, F. Ferri, Prototype selection for the nearest neighbor rule through proximity graphs, Pattern Recogn. Lett. 24(7) (2007) 1015-1022.
27. R. Setiono, B. Baesens, C. Mues, Recursive neural network rule extraction for data with mixed attributes, IEEE Trans. Neural Networks 19(2) (2008) 299-307.
28. R. Setiono, B. Baesens, C. Mues, A note on knowledge discovery using neural networks, its application to credit screening, Eur. J. Oper. Res. 192(1) (2009) 1009-1018.
29. R. Setiono, B. Baesens, C. Mues, Rule extraction from minimal neural networks for credit card screening, Int. J. Neural Syst. 21(4) (2011) 265-276.
30. M. R. Smith, T. Martinez, Improving classication accuracy by identifying, removing instances that should be misclassied, in Proc. 2011 Int. Joint Conf. Neural Networks, 2011, pp. 2690-2697.
31. M. Stepanova, L. Thomas, Survival analysis methods for personal loan data, Oper. Res. 50(2) (2002) 277-289.
32. H. H. Sung, R. Krishnan, Predicting repayment of the credit card debt, Comput. Oper. Res. 39 (2012) 765-773.
33. C.-F. Tsai, J.-W. Wu, Using neural network ensembles for bankruptcy prediction, credit scoring, Expert Syst. Appl. 34 (2008) 2639-2649.
34. D. Wang, Y. Li, A novel nonlinear RBF neural network ensemble model for nancial time series forecasting, in Proc. 3rd Int. Workshop on Advanced Computational Intelligence, 2010, pp. 86-90.
35. G. Wang, J. Ma, Study of corporate credit risk prediction based on integrating boosting, random subspace, Expert Syst. Appl. 38 (2011) 13871-13878.
36. G. Wang, J. Ma, L. Huang, K. Xu, Two credit scoring models based on dual strategy ensemble, Knowl. Based Syst. 26 (2012) 61-68.
37. D. West, Neural network credit scoring models, Comput. Oper. Res. 27 (2000) 1131-1152.
38. D. West, S. Dellana, J. Qian, Neural network ensemble strategies for nancial decision applications, Comput. Oper. Res. 32 (2005) 2543-2559.
39. T.-C. Wu, M.-F. Hsu, Credit risk assessment, decision making by a fusion approach, Knowl. Based Syst. 35 (2012) 102-110.
40. H. Xiang, S. G. Yang, Credit scoring model based on selective neural network ensemble, in Proc. 7th Int. Conf. Natural Computation, 2011, pp. 513-516.
41. L. Yan, R. Dodier, M. C. Mozer, R. Wolniewicz, Optimizing classier performance via the Wilcoxon-Mann-Whitney statistic, in Proc. Int. Conf. Machine Learning, 2003, pp. 848-855.
42. L. Yu, S. Wang, K. K. Lai, Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm, Energy Econ. 31(5) (2008) 2623-2635.
43. L. Yu, S. Wang, K. K. Lai, An intelligent-agent-based fuzzy group decision making model for nancial multicriteria decision support: The case of credit scoring, Eur. J. Oper. Res. 195 (2009) 942-959.
44. D. Zhang, X. Zhou, S. C. H. Leung, J. Zheng, Vertical bagging decision trees model fors credit scoring, Expert Syst. Appl. 37 (2010) 7838-7843.
45. J. Zurada, Introduction to Articial Neural Systems (West Publishing Company, St Paul, MN, 1992).