A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine

Expert Systems with Applications

Volumn 39, Issue 5, 2012, Pages 5325-5331

  Wang, Gang ^a,b,c   Ma, Jian ^c  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Bagging; Ensemble learning; Enterprise credit risk assessment; Random subspace; SVM

Indexed keywords

BAGGING; CREDIT RISK ASSESSMENT; ENSEMBLE LEARNING; RANDOM SUBSPACES; SVM;

FEATURE EXTRACTION; INDUSTRY; RATING; STATISTICAL METHODS; SUPPORT VECTOR MACHINES; VECTORS;

RISK ASSESSMENT;

EID: 84855863614     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.11.003     Document Type: Article

References (30)

1. B. Baesens, T. Van Gestel, S. Viaene, M. Stepanova, J. Suykens, and J. Vanthienen Benchmarking state-of-the-art classification algorithms for credit scoring Journal of the Operational Research Society 54 6 2003 1082 1088
2. L. Breiman Bagging predictors Machine Learning 24 2 1996 123 140
3. L. Breiman Random forests Machine Learning 45 1 2001 5 32
4. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 3 1995 273 297
5. V. Desai, J. Crook, and G. Overstreet A comparison of neural networks and linear scoring models in the credit union environment European Journal of Operations Research 95 1 1996 24 37
6. T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization Machine Learning 40 2 2000 139 157
7. J.H. Friedman Multivariate adaptive regression splines The Annals of Statistics 19 1 1991 1 141
8. D.J. Hand, and W.E. Henley Statistical classification methods in consumer credit scoring: A review Journal of the Royal Statistical Society: Series A (Statistics in Society) 160 3 1997 523 541
9. T.K. Ho The random subspace method for constructing decision forests IEEE Transactions on Pattern Analysis and Machine Intelligence 20 8 1998 832 844
10. Z. Huang, H. Chen, C.J. Hsu, W.H. Chen, and S.S. Wu Credit rating analysis with support vector machines and neural networks: A market comparative study Decision Support Systems 37 4 2004 543 558
11. C.L. Huang, M.C. Chen, and C.J. Wang Credit scoring with a data mining approach based on support vector machines Expert Systems with Applications 33 4 2007 847 856
12. C. Hung, and J.H. Chen A selective ensemble based on expected probabilities for bankruptcy prediction Expert Systems with Applications 36 3 2009 5297 5303
13. G. Karels, and A. Prakash Multivariate normality and forecasting of business bankruptcy Journal of Business Finance Accounting 14 4 1987 573 593
14. P. Makowski Credit scoring branches out Credit World 74 2 1985 30 37
15. L. Nanni, and A. Lumini An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring Expert Systems with Applications 36 2 2009 3028 3033
16. D. Opitz, and R. Maclin Popular ensemble methods: An empirical study Journal of Artificial Intelligence Research 11 1999 169 198
17. A.K. Reichert, C.C. Cho, and G.M. Wagner An examination of the conceptual issues involved in developing credit-scoring models Journal of Business and Economic Statistics 1 2 1983 101 114
18. R.E. Schapire The strength of weak learnability Machine Learning 5 2 1990 197 227
19. K.B. Schebesch, and R. Stecking Support vector machines for classifying and describing credit applicants: Detecting typical and critical regions Journal of the Operational Research Society 56 9 2005 1082 1088
20. K.S. Shin, and I. Han A case-based approach using inductive indexing for corporate bond rating Decision Support Systems 32 1 2001 41 52
21. L.C. Thomas A survey of credit and behavioral scoring: Forecasting financial risks of lending to customers International Journal of Forecasting 16 2 2000 149 172
22. 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
23. V. Vapnik The nature of statistical learning theory 1995 Springer-Verlag New York
24. G. Wang, J.X. Hao, J. Ma, and H.B. Jiang A comparative assessment of ensemble learning for credit scoring Expert Systems with Applications 38 1 2011 223 230
25. D. West Neural network credit scoring models Computers and Operations Research 27 11-12 2000 1131 1152
26. R. Wheeler, and S. Aitken Multiple algorithms for fraud detection Knowledge-Based Systems 13 2/3 2000 93 99
27. I.H. Witten, and E. Frank Data mining: Practical machine learning tools and techniques 2005 Morgan Kaufmann Publishers Boston
28. D.H. Wolpert Stacked generalization Neural Networks 5 2 1992 241 259
29. L.A. Yu, S.Y. Wang, and K.K. Lai Credit risk assessment with a multistage neural network ensemble learning approach Expert Systems with Applications 34 2 2008 1434 1444
30. Z.H. Zhou Ensemble L. Liu, T. Özsu, Encyclopedia of database systems 2009 Springer Berlin