Predicting business failure using support vector machines with straightforward wrapper: A re-sampling study

Expert Systems with Applications

Volumn 38, Issue 10, 2011, Pages 12747-12756

  Li, Hui ^a   Sun, Jie ^a  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

Author keywords

Business failure prediction (BFP); Feature ranking; Feature selection; Re sampling study; Support vector machine (SVM); Wrapper

Indexed keywords

BUSINESS FAILURE PREDICTION; FEATURE RANKING; FEATURE SELECTION; RESAMPLING; WRAPPER;

FEATURE EXTRACTION; FORECASTING; FUNCTION EVALUATION; GEARS; IMAGE RETRIEVAL; INVESTMENTS; PLANT SHUTDOWNS; RADIAL BASIS FUNCTION NETWORKS; SAMPLING;

SUPPORT VECTOR MACHINES;

EID: 79957989076     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.04.064     Document Type: Article

References (54)

1. H. Ahn, and K. Kim Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach Applied Soft Computing 9 2 2009 599 607
2. E.I. Altman Financial ratios, discriminant analysis and the prediction of corporate bankruptcy Journal of Finance 23 3 1968 589 609
3. R.S. Barr, and T.E. Siems Predicting bank failure using DEA to quantify management quality Financial Industrial Study 94 1 1994 1 31
4. W. Beaver Financial ratios as predictors of failure Empirical Research in Accounting: Selected Studies 5 1966 71 111
5. V.M. Becerra, R.K.H. Galvao, and M. Abou-Seada Neural and wavelet network models for financial distress classification Data Mining and Knowledge Discovery 11 2005 35 55 (Pubitemid 41202922)
6. G. Bloch, F. Lauer, G. Colin, and Y. Chamaillard Support vector regression from simulation data and few experimental samples Information Sciences 178 20 2008 3813 3827
7. X.B. Cao, Y.W. Xu, D. Chen, and H. Qiao Associated evolution of a support vector machine-based classifier for pedestrian detection Information Sciences 179 8 2009 1070 1077
8. A. Celikyilmaz, and I.B. Türken Fuzzy functions with support vector machines Information Sciences 177 23 2007 5163 5177 (Pubitemid 47409986)
9. Chang, C.-C., & Lin, C.-L. (2001). LibSVM: A library for support vector machines. Available at: .
10. N. Chauhan, V. Ravi, and D. Chandra Differential evolution trained wavelet networks: Application to bankruptcy prediction in banks Expert Systems with Applications 36 4 2009 7659 7665
11. W. Chen, and Y. Du Using neural networks and data mining techniques for the financial distress prediction model Expert Systems with Applications 36 2 2009 4075 4086
12. H. Chen, S. Huang, and C. Lin Alternative diagnosis of corporate bankruptcy: A neuro fuzzy approach Expert Systems with Applications 36 4 2009 7710 7720
13. N. Cristianini, and J. Shawe-Taylor An introduction to support vector machines 2000 Cambridge University Press Cambridge
14. A.I. Dimitras, R. Slowinski, R. Susmaga, and C. Zopounidis Business failure prediction using rough sets European Journal of Operational Research 114 1999 263 280
15. Y. Ding, X. Song, and Y. Zeng Forecasting financial condition of Chinese listed companies based on support vector machine Expert Systems with Applications 34 4 2008 3081 3089 (Pubitemid 351173811)
16. H. Frydman, E.I. Altman, and D. Kao Introducing recursive partitioning for financial classification: The case of financial distress Journal of Finance 40 1 1985 269 291
17. A. Gepp, K. Kumar, and S. Bhattacharya Business failure prediction using decision trees Journal of Forecasting 29 6 2010 536 555
18. I. Guyon, and A. Elisseeff An introduction to variable and feature selection Journal of Machine Learning Research 3 2003 1157 1182
19. W. Hardle, Y. Lee, D. Schafer, and Y. Yeh Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies Journal of Forecasting 28 6 2009 512 534
20. Y. Hu, and J. Ansell Retail default prediction by using sequential minimal optimization technique Journal of Forecasting 28 8 2009 651 666
21. Z. Hua, Y. Wang, X. Xu, B. Zhang, and L. Liang Predicting corporate financial distress based on integration of support vector machine and logistic regression Expert Systems with Applications 33 2 2007 434 440 (Pubitemid 46150047)
22. X.F. Hui, and J. Sun An application of support vector machine to companies' financial distress prediction V. Torra, Y. Narukawa, A. Valls, Modeling decisions for artificial intelligence 2006 Springer-Verlag Berlin
23. J. Jayadeva, R. Khemchandani, and S. Chandra Regularized least squares support vector regression for the simultaneous learning of a function and its derivatives Information Sciences 178 17 2008 3402 3414
24. H. Jo, I. Han, and H. Lee Bankruptcy prediction using case-based reasoning neural network and discriminant analysis for bankruptcy prediction Expert Systems with Applications 13 2 1997 97 108 (Pubitemid 127400547)
25. M. Kim, and D. Kang Ensemble with neural networks for bankruptcy prediction Expert Systems with Applications 37 4 2010 3373 3379
26. K. Kohavi, and G. John Wrappers for feature selection Artificial Intelligence 97 1-2 1997 273 324
27. Y. Lee Application of support vector machines to corporate credit rating prediction Expert Systems with Applications 33 1 2007 67 74 (Pubitemid 44959918)
28. H. Li, and J. Sun Ranking-order case-based reasoning for financial distress prediction Knowledge-Based Systems 21 8 2008 868 878
29. H. Li, and J. Sun Forecasting business failure in China using case-based reasoning with hybrid case respresentation Journal of Forecasting 29 5 2009 486 501
30. R. Lin, Y. Wang, C. Wu, and C. Chuang Developing a business failure prediction model via RST, GRA and CBR Expert Systems with Applications 36 2 2009 1593 1600
31. P. Lingras, and C. Butz Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification Information Sciences 177 18 2007 3782 3798 (Pubitemid 46961980)
32. S. Maldonado, and R. Weber A wrapper method for feature selection using support vector machines Information Sciences 179 13 2009 2208 2217
33. D. Martin Early warning of bank failure: A logit regression approach Journal of Banking and Finance 1 1977 249 276
34. J.-H. Min, and Y.-C. Lee Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters Expert Systems with Applications 28 2005 603 614 (Pubitemid 40583838)
35. J. Min, and Y. Lee A practical approach to credit scoring Expert Systems with Applications 35 4 2008 1762 1770
36. S.-H. Min, J.-M. Lee, and I. Han Hybrid genetic algorithms and support vector machines for bankruptcy prediction Expert Systems with Applications 31 2006 652 660 (Pubitemid 43841989)
37. J.A. Ohlson Financial ratios and the probabilistic prediction of bankruptcy Journal of Accounting Research 18 1980 109 131
38. M. Psillaki, I. Tsolas, and D. Margaritis Evaluation of credit risk based on firm performance European Journal of Operational Research 201 3 2010 873 881
39. P. Ravisankar, and V. Ravi Financial distress prediction in banks using group method of data handling neural network, counter propagation neural network and fuzzy ARTMAP Knowledge-Based Systems 23 8 2010 823 831
40. S. Sarkar, and R.S. Sriram Bayesian models for early warning of bank failures Management Science 47 11 2001 1457 1475 (Pubitemid 34192834)
41. 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
42. K.Y. Tam Neural network models and the prediction of bank bankruptcy Omega 19 5 1991 429 445
43. C.-F. Tsai Financial decision support using neural networks and support vector machines Expert Systems 25 4 2008 380 393
44. C.-F. Tsai, and J.-W. Wu Using neural network ensembles for bankruptcy prediction and credit scoring Expert Systems with Applications 34 4 2008 2639 2649 (Pubitemid 351173769)
45. F. Tseng, and Y. Hu Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks Expert Systems with Applications 37 3 2010 1846 1853
46. V.N. Vapnik Statistical learning theory 1998 Springer New York
47. G. Whalen A proportional hazards model of bank failure: An examination of its usefulness as an early warning tool Economic Review 27 1 1991 21 31
48. R.L. Wilson, and R. Sharda Bankruptcy prediction using neural networks Decision Support Systems 11 1994 545 557
49. C. Wu, G. Tzeng, and Y. Goo A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy Expert Systems with Applications 32 2 2007 397 408 (Pubitemid 44754927)
50. Z. Yang, M. Platt, and H. Platt Probabilistic neural networks in bankruptcy prediction Journal of Business Research 44 2 1999 67 74
51. C. Yeh, D. Chi, and M. Hsu A hybrid approach of DEA, rough set and support vector machines for business failure prediction Expert Systems with Applications 37 2 2010 1535 1541
52. J. Yoon, and Y. Kwon A practical approach to bankruptcy prediction for small business: Substituting the unavailable financial data for credit card sales information Expert Systems with Applications 37 5 2010 3624 3629
53. W. Yu, and X.-O. Li On-line fuzzy modeling via clustering and support vector machines Information Sciences 178 22 2008 4264 4279
54. J. Zhang, and Y. Wang A rough margin based support vector machine Information Sciences 178 9 2008 2204 2214