Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

Expert Systems with Applications

Volumn 58, Issue , 2016, Pages 93-101

  Ziȩba, MacIej ^a   Tomczak, Sebastian K ^a   Tomczak, Jakub M ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Bankruptcy prediction; Extreme gradient boosting; Imbalanced data; Synthetic features generation

Indexed keywords

ARTIFICIAL INTELLIGENCE; DECISION TREES; FORESTRY; HIGHER ORDER STATISTICS; REGRESSION ANALYSIS; TESTING;

ARITHMETIC OPERATIONS; BANKRUPTCY PREDICTION; GENERALIZED LINEAR MODEL; GRADIENT BOOSTING; IMBALANCED DATA; STATISTICAL HYPOTHESIS TESTING; STATISTICAL MODELING; SYNTHETIC FEATURES GENERATION;

FORECASTING;

EID: 84963595084     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2016.04.001     Document Type: Article

References (66)

1. E. Alfaro, N. García, M. Gámez, and D. Elizondo Bankruptcy forecasting: An empirical comparison of adaboost and neural networks Decision Support Systems 45 2008 110 122
2. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The Journal of Finance 23 1968 589 609
3. E.I. Altman, and E. Hotchkiss Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt 289 2010 John Wiley & Sons Hoboken, New Jersey
4. E.I. Altman, and B. Loris A financial early warning system for over-the-counter broker-dealers The Journal of Finance 31 1976 1201 1217
5. A. Aziz, D.C. Emanuel, and G.H. Lawson Bankruptcy prediction-an investigation of cash flow based models Journal of Management Studies 25 1988 419 437
6. B. Back, T. Laitinen, and K. Sere Neural networks and genetic algorithms for bankruptcy predictions Expert Systems with Applications 11 1996 407 413
7. W.H. Beaver Financial ratios as predictors of failure Journal of accounting research 1966 71 111
8. T.B. Bell, G.S. Ribar, and J. Verchio Neural nets versus logistic regression: a comparison of each model's ability to predict commercial bank failures Proceedings of the 1990 deloitte and touche/university of kansas symposium of auditing problems, lawrence, KS 1990 29 58
9. M. Blum Failing company discriminant analysis Journal of Accounting Research 12 1974 1 25
10. L. Breiman Bagging predictors Machine learning 24 1996 123 140
11. D.T. Cadden Neural networks and the mathematics of chaos-an investigation of these methodologies as accurate predictors of corporate bankruptcy First international conference on artificial intelligence applications on wall street 1991 IEEE 52 57
12. T. Chen, and T. He Higgs boson discovery with boosted trees JMLR: Workshop and conference proceedings 2015
13. T. Chen, and T. He xgboost: extreme gradient boosting. R package version 0.3-0 Technical Report 2015
14. P.K. Coats, and L.F. Fant A neural network approach to forecasting financial distress The Journal of Business Forecasting 10 1991 9
15. W.W. Cohen Fast effective rule induction Proceedings of the twelfth international conference on machine learning 1995 115 123
16. R.L. Constand, and R. Yazdipour Firm failure prediction models: a critique and a review of recent developments Advances in Entrepreneurial Finance 2011 Springer 185 204
17. C. Cortes, and V. Vapnik Support-vector networks Machine learning 20 1995 273 297
18. E.B. Deakin A discriminant analysis of predictors of business failure Journal of Accounting Research 1972 167 179
19. A. Dimitras, R. Slowinski, R. Susmaga, and C. Zopounidis Business failure prediction using rough sets European Journal of Operational Research 114 1999 263 280
20. R.O. Edmister An empirical test of financial ratio analysis for small business failure prediction Journal of Financial and Quantitative analysis 7 1972 1477 1493
21. W. Fan, S.J. Stolfo, J. Zhang, and P.K. Chan Adacost: Misclassification cost-sensitive boosting Icml 1999 97 105
22. P. Fitzpatrick A comparison of ratios of successful industrial enterprises with those of failed firms Certified Public Accountant 2 1932 598 605
23. Y. Freund, R.E. Schapire, and et al. Experiments with a new boosting algorithm Icml 1996 148 156
24. J.H. Friedman Greedy function approximation: a gradient boosting machine Annals of Statistics 2001 1189 1232
25. J.H. Friedman Stochastic gradient boosting Computational Statistics & Data Analysis 38 2002 367 378
26. M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 42 2012 463 484
27. R. Geng, I. Bose, and X. Chen Prediction of financial distress: An empirical study of listed chinese companies using data mining European Journal of Operational Research 241 2015 236 247
28. J.S. Grice, and M.T. Dugan Re-estimations of the Zmijewski and Ohlson bankruptcy prediction models Advances in Accounting 20 2003 77 93
29. T.K. Ho Random decision forests Document analysis and recognition, 1995., proceedings of the third international conference on 1995 IEEE 278 282
30. W. Hopwood, J.C. McKeown, and J.F. Mutchler A reexamination of auditor versus model accuracy within the context of the going-concern opinion decision Contemporary Accounting Research 10 1994 409 431
31. R. Johnson, and T. Zhang Learning nonlinear functions using regularized greedy forest Machine Learning 2011 arXiv preprint arXiv:1109.0887
32. J.E. Ketz The effect of general price-level adjustments on the predictive ability of financial ratios Journal of Accounting Research 16 1978 273 284
33. J. Kittler, M. Hatef, R.P. Duin, and J. Matas On combining classifiers Pattern Analysis and Machine Intelligence, IEEE Transactions on 20 1998 226 239
34. H.C. Koh Model predictions and auditor assessments of going concern status Accounting and Business Research 21 1991 331 338
35. H.C. Koh, and L.N. Killough The use of multiple discriminant analysis in the assessment of the going-concern status of an audit client Journal of Business Finance & Accounting 17 1990 179 192
36. A. Koster, N. Sondak, and W. Bourbia A business application of artificial neural network systems Journal of Computer Information Systems 31 1991 3 9
37. E.K. Laitinen Financial ratios and different failure processes Journal of Business Finance & Accounting 18 1991 649 673
38. M.Y.L. Li, and P. Miu A hybrid bankruptcy prediction model with dynamic loadings on accounting-ratio-based and market-based information: A binary quantile regression approach Journal of Empirical Finance 17 2010 818 833
39. R. Libby Accounting ratios and the prediction of failure: Some behavioral evidence Journal of Accounting Research 13 1975 150 161
40. C.L. Merwin Financing small corporations in five manufacturing industries NBER Books 1942 National Bureau of Economic Research, Inc New York 1926-1936
41. P.A. Meyer, and H.W. Pifer Prediction of bank failures Journal of Finance 24 1970 853 868
42. L. Nanni, and A. Lumini An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring Expert Systems with Applications 36 2009 3028 3033
43. J.A. Ohlson Financial ratios and the probabilistic prediction of bankruptcy Journal of Accounting Research 18 1980 109 131
44. R.H. Pettway, and J.F. Sinkey Establishing on-site bank examination priorities: An early-warning system using accounting and market information Journal of Finance 35 1980 137 150
45. H.D. Platt, and M.B. Platt Development of a class of stable predictive variables: the case of bankruptcy prediction Journal of Business Finance & Accounting 17 1990 31 51
46. H.D. Platt, M.B. Platt, and J.G. Pedersen Bankruptcy discrimination with real variables Journal of Business Finance & Accounting 21 1994 491 510
47. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufman Publishers San Francisco, CA, USA
48. M.A. Rujoub, D.M. Cook, and L.E. Hay Using cash flow ratios to predict business failures Journal of Managerial Issues 7 1995 75 90
49. L.M. Salchenberger, E. Cinar, and N.A. Lash Neural networks: A new tool for predicting thrift failures Decision Sciences 23 1992 899 916
50. C. Serrano-Cinca Self organizing neural networks for financial diagnosis Decision Support Systems 17 1996 227 238
51. K.S. Shin, T.S. Lee, and H.J. Kim An application of support vector machines in bankruptcy prediction model Expert Systems with Applications 28 2005 127 135
52. J.F. Sinkey A multivariate statistical analysis of the characteristics of problem banks The Journal of Finance 30 1975 21 36
53. K.Y. Tam Neural network models and the prediction of bank bankruptcy Omega 19 1991 429 445
54. K.Y. Tam, and M.Y. Kiang Managerial applications of neural networks: the case of bank failure predictions Management science 38 1992 926 947
55. J.M. Tomczak, and M. Ziȩba Probabilistic combination of classification rules and its application to medical diagnosis Machine Learning 101 2015 105 135
56. S. Tomczak Comparative analysis of liquidity ratios of bankrupt manufacturing companies Business and Economic Horizons (BEH) 10 2014 151 164
57. S. Tomczak Comparative analysis of the bankrupt companies of the sector of animal slaughtering and processing Equilibrium. Quarterly Journal of Economics and Economic Policy 9 2014 59 86
58. S. Tomczak The early warning system Journal of Management and Financial Sciences 7 2014 51 74
59. J.W. Wilcox A prediction of business failure using accounting data Journal of Accounting Research 11 1973 163 179
60. R.L. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decision Support Systems 11 1994 545 557
61. A. Winakor, and R.F. Smith Changes in financial structure of unsuccessful industrial companies Bureau of Business Research, Bulletin 51 1935
62. C. Zavgren The prediction of corporate failure: the state of the art Journal of Accounting Literature 2 1983 1 38
63. G. Zhang, M.Y. Hu, B.E. Patuwo, and D.C. Indro Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis European Journal of Operational Research 116 1999 16 32
64. Y. Zhang, S. Wang, and G. Ji A rule-based model for bankruptcy prediction based on an improved genetic ant colony algorithm Mathematical Problems in Engineering 2013 2013
65. M. Ziȩba, J.M. Tomczak, M. Lubicz, and J. Światek Boosted svm for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients Applied Soft Computing 14 2014 99 108
66. M.E. Zmijewski Methodological issues related to the estimation of financial distress prediction models Journal of Accounting Research 22 1984 59 82