Multiple classifier application to credit risk assessment

Expert Systems with Applications

Volumn 37, Issue 4, 2010, Pages 3326-3336

  Twala, Bhekisipho ^a  

^a DSI CSIR Nanotechnology Innovation Centre   (South Africa)

Author keywords

Credit risk prediction; Ensemble; Machine learning; Noise; Statistical pattern recognition; Supervised learning

Indexed keywords

ACTIVE AREA; ATTRIBUTE NOISE; CLASSIFICATION PERFORMANCE; CLASSIFIER ENSEMBLES; CREDIT RISK ASSESSMENT; CREDIT RISKS; DATA SETS; ENSEMBLE OF CLASSIFIERS; EXPERIMENTAL EVALUATION; INDIVIDUAL CLASSIFIERS; MACHINE-LEARNING; MULTIPLE CLASSIFIERS; PREDICTION ACCURACY; PREDICTIVE ACCURACY; QUALITY FACTORS; STATISTICAL PATTERN RECOGNITION;

CLASSIFIERS; EDUCATION; MATHEMATICAL MODELS; PATTERN RECOGNITION; RISK MANAGEMENT; ROBOT LEARNING; SUPERVISED LEARNING;

RISK ASSESSMENT;

EID: 71349085322     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.10.018     Document Type: Article

References (60)

1. Aha D.W. Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies 36 2 (1992) 267-287
2. Aha D.W., Kibbler D.W., and Albert M.K. Instance-based learning algorithms. Machine Learning 6 37 (1991) 37-66
3. Albright, H. T. (1994). Construction of polynomial classifier for consumer loan applications using genetic algorithms. Working paper, Department of Systems and Engineering, University of Virginia.
4. Altman E.I. Financial ratios, discriminant analysis and prediction of corporate bankruptcy. Journal of Finance 23 4 (1968) 589-609
5. Baesens B., Setiono R., Mues C.H., and Vanthienen J. Using neural network rule extraction and decision tables for credit risk evaluation. Management Science 49 (2003) 312-329
6. Bauer E., and Kohavi R. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36 1-2 (1999) 105-139
7. BIS. (2004). International convergence of capital measurement and capital standards: A revised framework. Basel Committee of Banking Supervision, Bank for International Settlements.
8. Blake, C., Keogh, E., & Merz, C. J. (1999). UCI respiratory of machine learning databases. University of California, Irvine, Department of Information and Computer Sciences. (January 2009).
9. Boyle, M., Crook, J. N., Hamilton, R., & Thomas, L. C. (1992). Methods for credit scoring applied to slow payers. In L. C. Thomas, J. N. Crook, & D. B. Edelman (Eds.), Credit scoring and credit control (pp. 75-90). Oxford: Clarendon.
10. Breiman L. Bagging predictors. Machine Learning 26 2 (1996) 123-140
11. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth.
12. Byrne B. Structural equation modelling with AMOS: Basic concepts, applications, and programming (2000), Lawrence Erlbaum, Mahwah, NJ
13. Carter C., and Catlett J. Assessing credit card applications using machine learning. IEEE Expert (1987) 71-79
14. Chatterjee S., and Barcun S. A nonparametric approach to credit screening. Journal of American Statistical Association 65 (1970) 50-154
15. Coffman, J. Y. (1986). The proper role of tree analysis in forecasting the risk behaviour of borrowers. MDS reports 3, 4, 7 and 9. Management decision systems. Atlanta.
16. Davis R.H., Edelman G.B., and Gammerman A.J. Machine learning algorithms for credit card applications. IMA Journal of Mathematics Applied Business and Industry 4 (1992) 43-51
17. Desai V.S., Convay J.N., and Overstreet G.A. Credit scoring models in credit-union environment using neural networks and genetic algorithms. IMA Journal of Mathematics Applied in Business and Industry 8 (1997) 323-346
18. Desai V.S., Crook J.N., and Overstreet G.A. A comparison of neural networks and linear scoring models in the credit environment. European Journal of Operations Research 85 (1996) 24-37
19. Dietterich T. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning 40 2 (2000) 139-158
20. Drucker H., Cortes C., Jackel L.D., Lecun Y., and Vapkin V. Boosting and other ensemble methods. Neural Computation 6 (1994) 1289-1301
21. Feldman D., and Gross S. Mortgage default: Classification tree analysis. Journal of Real Estate Finance and Economics 30 2005 (2005) 369-396
22. Forgaty T.C., and Ireson N.S. Evolving Bayesian classifier for credit control - A comparison with other machine learning methods. IMA Journal of Mathematics Applied in Business and Industry 5 (1993) 63-76
23. Freund Y., and Schapire R. A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computing and Systems 55 (1996) 119-139
24. Frydman H.E., Altman E.I., and Kao D. Introducing recursive partitioning for financial classification: The case of financial distress. Journal of Finance 40 1 (1985) 269-291
25. Hand, D. J. (2008). Private communication.
26. Hand D.J., and Henley W.E. Statistical classification methods in consumer credit scoring: A review. Journal of the Royal Statistical Society 160 (1997) 523-541
27. Hand D.J., and Vinciotti V. Choosing k for two-class nearest neighbour classifiers with unbalanced classes. Pattern Recognition Letters 24 (2003) 1555-1562
28. Henley, W. E. (1995). Statistical aspects of credit scoring. PhD dissertation, The Open University, Milton Keynes, UK.
29. Henley W.E., and Hand D.J. A k-nearest neighbour classifier for assessing consumer risk. Statistician 44 (1996) 77-95
30. Ho, T. K. (1995). Random decision forests. In Proceedings of the third international conference on document analysis and recognition (pp. 278-282).
31. Holmes C.C., and Adams N.M. A probabilistic nearest neighbour method for statistical pattern recognition. Journal of Royal Statistical Society, Series B 64 (2002) 295-306
32. Islam, M. J., Wu, Q. M. J., Ahmadi, M., & Sid-ahmed, M. A. (2007). Investigating the performance of naïve Bayes classifiers and k-nearest neighbor classifiers. In International conference on convergence information technology, November 21-23, 2007 (pp. 1541-1546).
33. Jensen H.L. Using neural networks for credit scoring. Managerial Finance 18 (1992) 15-26
34. Khalik, and El-Sheshai. Information choice and utilization in an experiment of default prediction. Journal of Accounting Research (1980) 325-342
35. Kirk R.E. Experimental design. 2nd ed. (1982), Brooks/Cole Publishing Company, Monterey, CA
36. Kittler J., Hatef M., Duin R.P.W., and Matas J. On combining classifiers. IEEE Transaction on Pattern Analysis and Machine Intelligence 20 3 (1998) 226-239
37. Kononenko, I. (1991). Semi-naïve Bayesian classifier. In Proceedings of European conference on artificial intelligence (pp. 206-219).
38. Lacher R.C., Oats P.K., Sharma S., and Fant L.F. A neural network for classifying the financial health of a firm. European Journal of Operational Research 85 (1995) 53-65
39. Leonard K.J. Empirical Bayes analysis of the commercial loans evaluation process. Statistics Probability Letters 18 (1993) 289-296
40. Mahlhotra R., and Malhotra D.K. Evaluating consumer loans using neural networks. OMEGA: The International Journal of Management Science 31 2 (2003) 83-96
41. Makowski P. Credit scoring branches out. Credit World 75 (1985) 30-37
42. MINITAB. (2002). MINITAB Statistical Software for Windows 9.0. PA, USA: MINITAB Inc.
43. Neumann D.E. An enhanced neural network technique for software risk analysis. IEEE Transactions on Software Engineering (2002) 904-912
44. Ong C.-S., Huang J.-J., and Tzeng G.-H. Building credit scoring models using genetic programming. Expert Systems with Applications 29 (2005) 41-47
45. Piramuthu S. Financial credit risk evaluation with neural and neurofuzzy systems. European Journal of Operational Research 112 (1999) 310-321
46. Quinlan J.R. C.4.5: Programs for machine learning (1993), Morgan Kauffman Publishers Inc., Los Altos, CA
47. Ripley B.D. Pattern recognition and neural networks (1992), Cambridge University Press/John Wiley, New York
48. Ripley B.D. Neural networks and related methods for classification. Journal of the Royal Statistical Society, Series B 56 3 (1994) 409-437
49. Ripley B.D. Pattern recognition and neural networks (1996), Cambridge University Press, Cambridge
50. Rosenberg E., and Gleit A. Quantitative methods in credit management: A survey. Operations Research 42 4 (1994) 589-613
51. Salchenberger L.M., Cinar E.M., and Lash N.A. Neural networks: A new tool for predicting thrift failures. Decision Sciences 23 (1992) 899-916
52. Schafer J.L. Analysis of incomplete multivariate data (1997), Chapman and Hall, London
53. Schapire R. The strength of weak learnability. Machine Learning 5 2 (1990) 197-227
54. Tam K.Y., and Kiang M.Y. Managerial applications of neural networks: The case of bank failure predictions. Management Science 38 7 (1992) 926-947
55. Twala, B. (2005). Effective techniques for handling incomplete data using decision trees. Unpublished PhD thesis, Open University, Milton Keynes, UK.
56. Twala B., Jones M.C., and Hand D.J. Good methods for coping with missing data in decision trees. Pattern Recognition Letters 29 (2008) 950-956
57. West D. Neural network credit scoring models. Computers & Operations Research 27 (2000) 1131-1152
58. Wiginton J.C. A note on the comparison of logit and discriminant models of consumer credit behavior. Journal of Financial and Quantitative Analysis 15 (1980) 757-770
59. Wolpert D. Stacked generalization. Neural Networks 5 2 (1992) 241-259
60. Yobas, M. B., Crook, J. N., & Ross, P. (1997). Credit scoring using neural and evolutionary techniques. Working paper 97/2, Credit Research Centre, University of Edinburgh.