Effects of feature construction on classification performance: An empirical study in bank failure prediction

Expert Systems with Applications

Volumn 36, Issue 2 PART 2, 2009, Pages 2633-2644

  Zhao, Huimin ^a   Sinha, Atish P ^a   Ge, Wei ^a  

^a UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

Bank failure prediction; Classification; Data mining; Feature construction; Financial ratios

Indexed keywords

CLASSIFICATION (OF INFORMATION); DECISION TREES; FINANCE; FORECASTING; NEAREST NEIGHBOR SEARCH; NEONATAL MONITORING; TREES (MATHEMATICS);

BANK FAILURE PREDICTION; CLASSIFICATION ALGORITHM; CLASSIFICATION PERFORMANCE; CLASSIFIER PERFORMANCE; EXPLANATORY VARIABLES; FEATURE CONSTRUCTION; FINANCIAL RATIOS; MISCLASSIFICATION COSTS;

DATA MINING;

EID: 56349155552     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.01.053     Document Type: Article

References (73)

1. Aha D., and Kibler D. Instance-based learning algorithms. Machine Learning 6 1 (1991) 37-66
2. Altman E.I., Marco G., and Varetto F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of Banking and Finance 18 (1994) 505-529
3. Anandarajan M., Lee P., and Anandarajan A. Bankruptcy prediction of financially stressed firms: An examination of the predictive accuracy of artificial neural networks. International Journal of Intelligent Systems in Accounting, Finance & Management 10 2 (2001) 69-81
4. Barr, R. S., & Siems, T. E. (1994). Predicting bank failure using DEA to quantify management quality. Financial Industry Studies Working Paper, Federal Reserve Bank of Dallas (pp. 1-31).
5. Berry, R. H., & Trigueiros, D. (1993). Applying neural networks to the extraction of knowledge from accounting reports: A classification study. In Trippi, R. R. & Turban, E. (Eds.) Neural networks in finance and investing (pp. 103-123). Chicago, IL: Probus Publishing.
6. Bryant S.M. A case-based reasoning approach to bankruptcy prediction modeling. International Journal of Intelligent Systems in Accounting Finance & Management 6 3 (1997) 195-214
7. Chauvin Y., and Rumelhart D.E. Backpropagation: Theory, Architectures, and Applications (1995), Lawrence Erlbaum Association, Hillsdale NJ
8. Cherkassky V., and Lari-Najafi H. Data representation for diagnostic neural networks. IEEE Expert 7 5 (1992) 43-53
9. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. (1988), Erlbaum, Hillsdale, NJ
10. Cole R., and Gunther J. Separating the likelihood and timing of bank failure. Journal of Banking and Finance 19 6 (1995) 1073-1089
11. Cover T.M., and Van Campenhout J.M. On the possible orderings in the measurement selection problem. IEEE Transactions on System, Man and Cybernetics 7 9 (1977) 657-661
12. Dash M., and Liu H. Feature selection for classification. Intelligent Data Analysis 1 (1997) 131-156
13. Demirgue-Kent, A. (1989). Modeling large commercial-bank failures: A simultaneous equation analysis. Working paper 8905, Federal Reserve Bank of Cleveland: Cleveland, OH.
14. Dybowski R., Laskey K.B., Myers J.W., and Parsons S. Introduction to the special issue on the fusion of domain knowledge with data for decision support. Journal of Machine Learning Research 4 (2003) 293-294
15. Elkan, C. (2001). The foundations of cost-sensitive learning. In proceedings of the 17th international joint conference on artificial intelligence (pp. 973-978), Seattle, WA.
16. Fayyad U., Piatetsky-Shapiro G., and Smyth P. The KDD process for extracting useful knowledge from volumes of data. Communications of ACM 39 11 (1996) 27-34
17. Feelders A., Daniels H., and Holsheimer M. Methodological and practical aspects of data mining. Information and Management 37 (2000) 271-281
18. Frydman H., Altman E., and Kao D. Introducing recursive partitioning for financial classification: the case of financial distress. Journal of Finance 40 1 (1985) 269-291
19. Gajewski, G.R. (1989). Assessing the risk of bank failure. In Proceedings from a conference on bank structure and competition (pp. 432-456), Federal Reserve Bank of Chicago, Chicago, IL.
20. Hand D., Mannila H., and Smyth P. Principles of data mining (2001), MIT Press, Cambridge, MA
21. Hosmer D.W., and Lemeshow S. Applied logistic regression. 2nd ed. (2000), John Wiley & Sons Inc
22. John, G.H., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and subset selection problem. In proceedings for the 11th international conference on machine learning.
23. Kim C.N., and McLeod R. Expert, linear models, and nonlinear models of expert decision making in bankruptcy prediction: A lens model analysis. Journal of Management Information Systems 16 1 (1999) 189-206
24. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Mellish C.S. (Ed). Proceedings of the 14th international joint conference on artificial intelligence (1995), Morgan Kaufmann, San Mateo, CA 1137-1143
25. Koller, D., & Sahami, M. (1996). Toward optimal feature selection. In Proceedings for the 13th international conference on machine learning.
26. Korobow, L., Stuhr, D.P., & Martin, D. (1977). A nationwide test of early warning research in banking. Federal Reserve Bank of New York, Quarterly Review, Autumn, (pp. 37-52).
27. Lane W.R., Looney S.W., and Wansley J.W. An application of the cox proportional hazards model to bank failure. Journal of Banking and Finance 10 (1986) 511-531
28. Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. In proceedings of the 10th conference on uncertainty in artificial intelligence.
29. Liu H., and Motoda H. Feature extraction, construction, and selection: A data ming perspective (1998), Kluwer
30. Looney S.W., Wansley J.W., and Lane W.R. An examination of misclassification with bank failure prediction models. Journal of Economics and Business 41 (1989) 327-336
31. Markovitch S., and Rosenstein D. Feature generation using general constructor functions. Marching Learning 49 1 (2002) 59-98
32. Martin D. Early warning of bank failure: Logit regression approach. Journal of Banking and Finance 1 (1977) 249-276
33. Matheus, C.J., & Rendell, L. (1989). Constructive induction in decision trees. In Proceedings of 11th IJCAI.
34. Mckee T.E. Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting Finance & Management 9 3 (2000) 159-173
35. Mucciardi A.N., and Gose E.E. A comparison of seven techniques for choosing subsets of pattern recognition properties. IEEE Transactions on Computer C-20 (1971) 1023
36. Murthy S.K. Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery 2 (1998) 345-389
37. Nanda S., and Pendharkar P. Linear models for minimizing misclassification costs in bankruptcy prediction. International Journal of Intelligent Systems in Accounting Finance & Management 10 3 (2001) 155-168
38. OLeary D.E. Using neural networks to predict corporate failure. International Journal of Intelligent Systems in Accounting, Finance & Management 7 3 (1998) 187-197
39. Pakath R., and Zaveri J.S. Specifying critical inputs in a genetic algorithm-driven decision support system: an automated facility. Decision Sciences 26 6 (1995) 749-779
40. Pettway R.H., and Sinkey Jr. J. Establishing on-site bank examination priorities: An early warning system using accounting and marketing information. Journal of Finance 35 1 (1980) 137-150
41. Piramuthu S., Ragavan H., and Shaw M.J. Using feature construction to improve performance of neural networks. Management Science 44 3 (1998) 416-430
42. Pompe P.P.M., and Bilderbeek J. Bankruptcy prediction: the influence of the year prior to failure selected for model building and the effects in a period of economic decline. International Journal of Intelligent Systems in Accounting, Finance and Management 13 2 (2005) 95-112
43. Provost, F., Fawcett, T., & Kohavi, R. (1998). The case against accuracy estimation for comparing induction algorithms. In proceedings of the 15th international conference on machine learning (pp. 445-453), Madison, WI.
44. Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
45. Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann.
46. Radcliffe N.J., and Surry P.D. Fundamental limitations on search algorithms: Evolutionary computing in perspective. In: Leeuwen J.V. (Ed). Computer Science Today: Recent Trends and Developments: Lecture Notes in Computer Science (1995), Springer-Verlag, London
47. Ragavan, H., Rendell, M., Shaw, M., & Tessmer, A. (1993). Complex concept acquisition through directed search and feature caching, and practical results in a financial domain. In Proceedings of 13th IJCAI.
48. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and McClelland J.L. (Eds). Parallel distributed processing (1986), MIT Press, Cambridge, MA 318-362
49. Ryu Y.U., and Yue W.T. Firm bankruptcy prediction: Experimental comparison of isotonic separation and other classification approaches. IEEE Transactions on Systems, Man, and Cybernetics: Part A 35 5 (2005) 727-737
50. Salcedo-Sanz S., DePrado-Cumplido M., Segovia-Vargas M.J., Pérez-Cruz F., and Bousoño-Calzón C. Feature selection methods involving support vector machines for prediction of insolvency in non-life insurance companies. International Journal of Intelligent Systems in Accounting, Finance and Management 12 4 (2004) 261-281
51. Santomero A.M., and Vinso J.D. Estimating the probability of failure by commercial banks. Journal of Banking and Finance 1 (1977) 185-205
52. Sarkar S., and Sriram R.S. Bayesian models for early warning of bank failures. Management Science 47 11 (2001) 1457-1475
53. Sinha A.P., and May J.H. Evaluating and tuning predictive data mining models using receiver operating characteristic curves. Journal of Management Information Systems 21 3 (2005) 249-280
54. Sinkey J. A multivariate statistical analysis of the characteristics of problem banks. Journal of Finance 31 1 (1975) 21-38
55. Sinkey J. Identifying large problem/failed banks: The case of the Franklin National Bank of New York. Journal of Financial and Quantitative Analysis 12 (1977) 779-800
56. Sinkey J. Identifying problem banks: how do the banking authorities measure a bank's risk exposure. Journal of Money, Credit and Banking 10 (1978) 184-193
57. Sinkey, J. (1979). Problem and failed institutions in the commercial banking industry. Contemporary studies in economic and financial analysis, Vol. 4. JAI Press, Greenwich, Conn.
58. Stuhr, D.P., & Van Wicklen, R. (1974). Rating and financial condition of banks: a statistical approach to aid bank supervision. Monthly Review, Federal Reserve Bank of New York, September (pp. 233-238).
59. Sung T.K., Chang N., and Lee G. Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems 16 1 (1999) 63-85
60. Swicegood P., and Clark J.A. Off-site monitoring systems for predicting bank underperformance: A comparison of neural networks, discriminant analysis, and professional human judgment. International Journal of Intelligent Systems in Accounting, Finance & Management 10 3 (2001) 169-186
61. Tahai A., Walczak S., and Rigsby J.T. Improving artificial neural network performance through input variable selection. In: Siegel P., Omer K., deKorvin A., and Zebda A. (Eds). Applications of fuzzy sets and the theory of evidence to accounting II (1998), JAI Press, Stamford, CT 277-292
62. 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
63. Thomson, J.B. (1991). Predicting bank failures in the 1980s. Economic Review (Q I), (9-20).
64. Weiss S.M., and Indurkhya N. Predictive data mining: A practical guide (1998), Morgan Kaufmann, San Francisco, CA
65. Weiss, S.M., & Kapouleas, I. (1989). An empirical comparison of pattern recognition, neural nets, and machine learning classification methods. In Proceedings of the eleventh international joint conference on artificial intelligence (pp. 781-787). San Francisco: Springer.
66. Weiss S.M., and Kulikowski C.A. Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems (1991), Morgan Kaufmann, San Mateo, CA
67. West R.G. A factor-analytic approach to bank condition. Journal of Banking and Finance 9 (1985) 254-266
68. Whalen G. A proportional hazards model of bank failure: An examination of its usefulness as an early warning tool. Economic Review, Federal Reserve Bank of Cleveland 27 (1991) 21-31
69. Whalen, G., & Thomson, J.B. (1988). Using financial data to identify changes in bank condition. Economic Review, (Q II), (pp. 17-26).
70. Witten I.H., and Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufman, San Francisco, CA
71. Wolpert D.H. The relationship between PAC, the statistical physics framework, the Bayesian framework, and the VC framework. In: Wolpert D.H. (Ed). The mathematics of generalization - Proceedings of the SFI/CNLS workshop on formal approaches to supervised learning (1994), Addison-Wesley 117-214
72. Zhao, H. (2008). Instance weighting versus threshold adjusting for cost-sensitive classification. Knowledge and information systems. doi:10.1007/s10115-007-0079-1.
73. Zheng Z. Constructing X-of-N attributes for decision tree learning. Machine Learning 40 1 (2000) 35-75