Could decision trees improve the classification accuracy and interpretability of loan granting decisions?

Proceedings of the Annual Hawaii International Conference on System Sciences

Volumn , Issue , 2010, Pages

  Zurada, Jozef ^a  

^a Department of Computer Information Systems   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

10-FOLD CROSS-VALIDATION; CASE-BASE REASONINGS; CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; CUT-OFF; DATA SETS; ERROR ESTIMATES; FINANCIAL INSTITUTION; HISTORICAL DATA; IF-THEN RULES; INTERPRETABILITY; LOGISTIC REGRESSIONS; RADIAL BASIS FUNCTION NEURAL NETWORKS;

CLASSIFICATION (OF INFORMATION); DECISION TREES; FINANCE; RADIAL BASIS FUNCTION NETWORKS; STATISTICAL TESTS;

NEURAL NETWORKS;

EID: 77951744572     PISSN: 15301605     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HICSS.2010.124     Document Type: Conference Paper

References (23)

1. D.K. Barney, O.F. Graves, and J.D. Johnson, "The Farmers Home Administration and Farm Debt Failure Prediction", Journal of Accounting and Public Policy, 1999, Vol. 18, pp. 99-139.
2. V.S. Desai, J.N. Crook, and G.A. Overstreet, Jr, "A Comparison of Neural Networks and Linear Scoring Models in the Credit Union Environment", European Journal of Operation Research, 1996, Vol. 95, pp. 24-37.
3. L. Fahrmeir, and A. Hamerle, Multivariate Statistical Modeling Based on Generalized Linear Programs, Springer-Verlag, Berlin, 1994.
4. D. Feldman, and S. Gross, "Mortgage Default: Classification Tree Analysis", Journal of Real Estate Finance and Economics, 2005, Vol. 30, pp. 369-396.
5. P. Giudici, Applied Data Mining: Statistical Methods for Business and Industry, John Wiley & Sons Ltd., Chichester, West Sussex, England, 2003.
6. J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA, 2001.
7. D. Hand, and W. Henley, "Statistical Classification Method in Consumer Scoring: A Review", Journal of the Royal Statistical Society, Series A (Statistics in Society), 1997, Vol. 160, pp. 523-541. (Pubitemid 127130586)
8. Y-H. Huang, C-M Hung, and H.C Jiau, "Evaluation of Neural Networks and Data mining Methods on a Credit Assessment Task for Class Imbalance Problem", Nonlinear Analysis: Real Worlds Applications, 2006, 7, pp. 720-747.
9. I. Jagielska, C. Matthews, and T. Whitfort, "An Investigation into the Application of Neural Networks, Fuzzy Logic, Genetic Algorithms, and Rough Sets to Automated Knowledge Acquisition for Classification Problems", Neurocomputing, 1999, Vol. 24, pp. 37-54.
10. T.M. Mitchell, Machine Learning, WCB/McGraw-Hill, Boston, Massachusetts, 1997.
11. S. Piramuthu, "Financial Credit-Risk Evaluation with Neural and Neurofuzzy Systems", European Journal of Operational Research, 1999, Vol. 112, pp. 310-321.
12. S. Piramuthu, "Feature Selection for Financial Credit-Risk Evaluation Decisions", INFORMS Journal on Computing, 1999, Vol. 11, No. 3, pp. 258-266.
13. J.R. Quinlan, "Simplifying Decision Trees", International Journal of Man-Machine Studies, 1987, Vol. 27, pp. 221-234.
14. E. Rosenberg, and A. Gleidt, "Quantitative Methods in Credit Management: A Survey", Operations Research, 1994, Vol. 42, pp. 589-613.
15. P-N Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison-Wesley, 2006.
16. L.C. Thomas, "A Survey of Credit and Behavioral Scoring: Forecasting Financial Risk of Lending to Consumers", International Journal of Forecasting, 2000, Vol. 16, pp. 149-172.
17. M. Vojtek, and E. Kocenda, "Credit Scoring Methods", Czech Journal of Economics and Finance, 2006, Vol. 56, Issue 3-4, pp. 152-167.
18. D. West, "Neural Network Credit Scoring Models", Computers & Operations Research, 2000, Vol. 27, pp. 1131-1152. (Pubitemid 30398142)
19. I.H. Witten, and E. Frank, Data Mining: Practical Learning Tools and Techniques Morgan Kaufmann Publishers, 2005.
20. B. Yang, L.X. Li, H. Ji, and J. Xu, "An Early Warning System for Loan Risk Assessment Using Artificial Neural Networks", Knowledge-Based Systems, 2001, Vol. 14, pp. 303-306. (Pubitemid 32707396)
21. Y. Yang, "Adaptive Credit Scoring with Kernel Learning Methods", European Journal of Operational Research, 2007, Vol. 183, Issue 3, pp. 1521-1536. (Pubitemid 47030754)
22. J. Zurada, and M. Zurada, "How Secure are "Good Loans": Validating Loan-Granting Decisions and Predicting Default Rates on Consumer Loans", Review of Business Information Systems, 2002, 6(3), pp. 65-83.
23. J. Zurada, "Rule Induction Methods for Credit Scoring", Review of Business Information Systems, 2007, Vol. 11, 2, pp. 11-22.