From linear to non-linear kernel based classifiers for bankruptcy prediction

Neurocomputing

Volumn 73, Issue 16-18, 2010, Pages 2955-2970

  Van Gestel, Tony ^a   Baesens, Bart ^b,c   Martens, David ^b,d  

^a Dexia Group   (Belgium)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c UNIVERSITY COLLEGE GHENT   (Belgium)

^d UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Bankruptcy prediction; Kernel classifiers; Least squares support vector machine

Indexed keywords

ACCOUNTING STANDARDS; AREA UNDER THE ROC CURVE; BANKRUPTCY PREDICTION; BASEL II; BINARY CLASSIFICATION; CLASSIFICATION ACCURACY; CLASSIFICATION TECHNIQUE; FINANCIAL DISTRESS; FISHER DISCRIMINANT ANALYSIS; HIGH IMPACT; INPUT SELECTION; KERNEL CLASSIFIERS; LEAST SQUARES SUPPORT VECTOR MACHINES; LOGISTIC REGRESSIONS; NON-LINEAR; QUADRATIC DISCRIMINANT ANALYSIS; REAL LIFE DATASETS; RELATIVE IMPORTANCE; RELATIVE RANKINGS; TEST INSTANCES;

CLASSIFIERS; DISCRIMINANT ANALYSIS; FISHER INFORMATION MATRIX; PROFITABILITY; SUPPORT VECTOR MACHINES;

FORECASTING;

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CLASSIFIER; DISCRIMINANT ANALYSIS; FINANCIAL MANAGEMENT; KERNEL METHOD; LINEAR SYSTEM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PERCEPTRON; PREDICTION; PRIORITY JOURNAL; PROBABILITY; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 78649445034     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.07.002     Document Type: Article

References (85)

1. Ahn B.S., Cho S.S., Kim C.Y. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications 2000, 18(2):65-74.
2. Altman E. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 1968, 23:589-609.
3. E. Altman, Corporate financial distress and bankruptcy: a complete guide predicting and avoiding distress and profiting from bankruptcy, Wiley finance edition, 1993.
4. Altman E., Marco G., Varetto F. Corporate distress diagnosis: comparisons using linear discriminant analysis and neural networks (the italian experience). Journal of Banking and Finance 1994, 18:505-529.
5. E.I. Altman, E.R., J. Sinkey, Applications of Classification Techniques in Business, Banking and Finance, JAI Press, 1981.
6. Atiya A. Bankruptcy prediction for credit risk using neural networks: a survey and new results. IEEE Transactions on Neural Networks 2001, 12(4):929-935.
7. Baesens B., Gestel T.V., Viaene S., Stepanova M., Suykens J., Vanthienen J. Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 2003, 54(6):627-635.
8. Baesens B., Setiono R., Mues C., Vanthienen J. Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science 2003, 49(3):312-329.
9. Baudat G., Anouar F. Generalized discriminant analysis using a kernel approach. Neural Computation 2000, 12:2385-2404.
10. Beaver W. Financial ratios as predictors of failure. Empirical Research, in Accounting Selected Studies supplement to the Journal of Accounting 1966, 5:71-111.
11. Becerra V.M., Galvao R.K., Abou-Seada M. Neural and wavelet network models for financial distress classification. Data Mining and Knowledge Discovery 2005, 11(1):35-55.
12. Bishop C. Neural Networks for Pattern Recognition 1995, Oxford University Press.
13. Boritz J., Kennedy D. Effectiveness of neural network types for prediction of business failure. Expert Systems With Applications 1995, 9(4):503-512.
14. Boritz J., Kennedy D., Albuquerque A. Predicting corporate failure using a neural network approach. International Journal of Intelligent Systems in Accounting, Finance and Management 1995, 4(2):95-111.
15. B. Boser, I. Guyon, V. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, ACM, 1992, pp. 144-152.
16. Brown P. Centering and scaling in ridge regression. Technometrics 1977, 19:35-36.
17. Campbell J., Lo A., MacKinlay A. The Econometrics of Financial Markets 1997, Princeton University Press, Princeton, New Jersey.
18. Campbell N. Shrunken estimators in discriminant and canonical variate analysis. Applied Statistics 1980, 29(1):5-14.
19. Chauhan N., Ravi V., Chandra D.K. Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Systems with Applications 2009, 36(4):7659-7665.
20. Cressie N. Statistics for Spatial Data 1993, John Wiley & Sons, New York.
21. Cristianini N., Shawe-Taylor J. An Introduction to Support Vector Machines 2000, Cambridge University Press.
22. de Andres J., Landajo M., Lorca P. Forecasting business profitability by using classification techniques: a comparative analysis based on a spanish case. European Journal of Operational Research 2005, 167(2):518-542.
23. De Long E., De Long D., Clarke-Pearson D. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988, 44:837-845.
24. Dietterich T. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation 1998, 10(7):1895-1924.
25. Dimitras A.I., Z.S., Zopounidis C. A survey of business failures with an emphasis on prediction methods and industrial applications. European Journal of Operational Research 1996, 90:487-513.
26. DiPillo P. The application of bias to discriminant analysis. Communications in Statistics, Theory and Methods A 1976, 5:843-854.
27. Duda R., Hart P. Pattern Classification and Scene Analysis 1973, John Wiley, New York.
28. Edmister R.O. An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis 1972, 7(02):1477-1493.
29. Efron E. The efficiency of logistic regression compared to normal discriminant analysis. Journal of the American Statistical Society 1975, 70:892-898.
30. Egan J. Signal Detection Theory and ROC Analysis, Series in Cognition and Perception 1975, Academic Press, New York.
31. Everitt B. The Analysis of Contingency Tables 1977, Chapman & Hall, London.
32. Farebrother R. Partitioned ridge regression. Technometrics 1978, 20:121-122.
33. Fisher R. The use of multiple measurements in taxonomic problems. Annals of Eugenics 1936, 7:179-188.
34. Friedman J. Regularized discriminant analysis. Journal of the American Statistical Association 1989, 84:165-175.
35. Hanley J., McNeil B. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983, 148:839-843.
36. Härdle W. Applied Nonparametric Regression 1990, Cambridge University Press, Cambridge, UK.
37. Hoerl A. Application of ridge analysis to regression problems. Chemical Engineering Progress 1962, 58:54-59.
38. Hoerl A., Kennard R. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970, 12:55-67.
39. Hornik K., S.M., White H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 2:359-366.
40. Hotelling H. Relations between two sets of variates. Biometrica 1936, 28:321-377.
41. Hu Y.-C., Tseng F.-M. Functional-link net with fuzzy integral for bankruptcy prediction. Neurocomputing 2007, 70(16-18):2959-2968.
42. Hung C., Chen J.-H. A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Systems with Applications 2009, 36(3):5297-5303.
43. Hutchinson J., Lo A., Poggio T. A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance 1994, 49:851-889.
44. Kiviluoto K. Predicting bankruptcies with the self-organizing map. Neurocomputing 1998, 21(1-3):191-201.
45. Leshno M., Spector Y. Neural network prediction analysis: the bankruptcy case. Neurocomputing 1996, 10(2):125-147.
46. Lim T., Loh W., Shih Y. A comparison of prediction accuracy. Complexity and training time of thirty-three old and new classification algorithms, Machine Learning 2000, 40(3):203-228.
47. Lin T.-H. A cross model study of corporate financial distress prediction in taiwan: multiple discriminant analysis logit, probit, and neural networks models. Neurocomputing 2009, 72(16-18):3507-3516.
48. MacKay D. Bayesian interpolation. Neural Computation 1992, 4:415-447.
49. D. MacKay, Introduction to gaussian processes, in: C. Bishop (Ed.), Neural Networks and Machine Learning, Springer NATO-ASI Series F, vol. 168, 1998, pp. 133-165.
50. Martens D., Baesens B., Van Gestel T., Vanthienen J. Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research 2007, 183(3):1466-1476.
51. Martens D., Bruynseels L., Baesens B., Willekens M., Vanthienen J. Predicting going concern opinion with data mining. Decision Support Systems 2008, 45:765-777.
52. Martens D., Van Gestel T., Baesens B. Decompositional rule extraction from support vector machines by active learning. IEEE Transactions on Knowledge and Data Engineering 2009, 21(2):178-191.
53. McCullagh P., Nelder J. Generalized Linear Models 1989, Chapman & Hall, London.
54. Mercer J. Functions of positive and negative type and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London 1909, 209:415-446.
55. Michie D., Spiegelhalter D., Taylor C.E. Machine Learning, Neural and Statistical Classification 1994, Ellis Horwood.
56. Nadaraya E. On estimating regression. Theory of Probability and its Application 1964, 10:186-190.
57. Nanda S., Pendharkar P. Linear models for minimizing misclassification costs in bankruptcy prediction. International Journal of Intelligent Systems in Accounting, Finance and Management 2001, 10:155-168.
58. Ohlson J. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research 1980, 18:109-131.
59. Parzen E. On estimation of a probability density function and mode. Annals Mathematical Statistics 1962, 33(1065-1076):23-25.
60. Poggio T., Girosi F. Networks for approximation and learning. Proceedings of the IEEE 1990, 78:1481-1497.
61. F. Provost, T. Fawcett, R. Kohavi, The case against accuracy estimation for comparing classifiers, in: J. Shavlik (Ed.), Proceedings of the 15th International Conference on Machine Learning (ICML), Morgan Kaufmann, San Francisco, CA, USA, 1998, pp. 445-453.
62. Rao P. Nonparametric Functional Estimation 1983, Academic Press, New York.
63. Ravi V., Kurniawan H., Thai P.N.K., Kumar P.R. Soft computing system for bank performance prediction. Applied Soft Computing 2008, 8(1):305-315.
64. Ripley B.D. Neural networks and related methods for classification. Journal of the Royal Statistical Society B 1994, 56:409-456.
65. Schölkopf B., Smola A. Learning with Kernels 2002, MIT Press, Cambridge, MA.
66. Serrano Cinca C. Feedforward neural networks in the classification of financial information. European Journal of Finance 1997, 3(3):183-202.
67. Sheskin D. Handbook of Parametric and Nonparametric Statistical Procedures 2000, Chapman & Hall. second ed.
68. Suykens J., Van Gestel T., DeBrabanter J., DeMoor B.J.V. Least Squares Support Vector Machines 2002, World Scientific.
69. Swets J. Roc analysis applied to the evaluation of medical imaging techniques. Investigative Radiology 1979, 14:109-121.
70. Swets J., Pickett R. Evaluation of Diagnostic Systems: Methods from Signal Detection Theory 1982, Academic Press, New York.
71. Tam K., Kian M. Managerial applications of neural networks: the case of bank failure predictions. Management Science 1992, 38(7):926-947.
72. Tsai C.-F., Wu J.-W. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications 2008, 34(4):2639-2649.
73. Tung W.L., Quek H.C., Cheng P. Genso-ews: a novel neural-fuzzy based early warning system for predicting bank failures. Neural Networks 2004, 17(4):567-587.
74. Van Gestel T., Baesens B., Suykens J., Poel D.V., Baestaens D.-E., Willekens M. Bayesian kernel based classification for financial distress detection. European Journal of Operational Research 2005, 172(3):979-1003.
75. Van Gestel T., Martens D., Baesens B., Feremans D., Huysmans J., Vanthienen J. Forecasting and analyzing insurance companies' ratings. International Journal of Forecasting 2007, 23(3):513-529.
76. Van Gestel T., Suykens J., Baestaens D.-E., Lambrechts A.G.L., Vandaele B., DeMoor B., Vandewalle J. Predicting financial time series using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks Special Issue on Financial Engineering 2001, 12:809-821.
77. Van Gestel T., Suykens J., Lanckriet G., Lambrechts A., DeMoor B., Vandewalle J. A bayesian framework for least squares support vector machine classifiers, gaussian processes and kernel fisher discriminant analysis. Neural Computation 2002, 14:1115-1147.
78. Van Gestel T., Suykens J.A.K., Baesens B., Viaene S., Vanthienen J., Dedene G., DeMoor B., Vandewalle J. Benchmarking least squares support vector machine classifiers. Machine Learning 2004, 54(1):5-32.
79. Vapnik V. Statistical Learning Theory 1998, Wiley, New York.
80. A. Verikas, Z. Kalsyte, M. Bacauskiene, A. Gelzinis, Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey, Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2009.
81. Vinod H. Canonical ridge and econometrics of joint production. Journal of Econometrics 1976, 4:147-166.
82. Wahba G. Support vector machines. Advances in Kernel Methods-Support Vector Learning, Reproducing Kernel Hilbert Spaces, and Randomized GACV 1998, 69-87. MIT Press, (Chapter 6). B.B.C. Scholköpf, A. Smola (Eds.).
83. Watson G. Smooth regression analysis. Sankhya. The Indian Journal of Statistics, Series A 1964, 26:359-372.
84. Wilson R., Sharda R. Bankruptcy prediction using neural networks. Decision Support Systems 1994, 11:545-557.
85. Zhang G., Hu M., Patuwo B., Indro D. Artificial neural networks in bankruptcy prediction: general framework and cross-validation analysis. European Journal of Operational Research 1999, 116:16-32.