Least squares support vector machines ensemble models for credit scoring

Expert Systems with Applications

Volumn 37, Issue 1, 2010, Pages 127-133

  Zhou, Ligang ^a   Lai, Kin Keung ^a   Yu, Lean ^b  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b INSTITUTE OF SYSTEMS SCIENCE   (China)

Author keywords

Credit scoring; Ensemble model; Support vector machines

Indexed keywords

BASEL II; CLASSIFICATION ,; CREDIT RISK ASSESSMENT; CREDIT SCORING; CREDIT SCORING MODEL; ENSEMBLE MODEL; ENSEMBLE MODELS; ENSEMBLE STRATEGIES; FINANCIAL CRISIS; FINANCIAL INSTITUTION; FINANCIAL RISK MANAGEMENT; INDUCTIVE BIAS; LEAST SQUARES SUPPORT VECTOR MACHINES; PARAMETERS SETTING; REAL-WORLD DATASETS; SINGLE SUPPORT; TRAINING SAMPLE;

IMAGE RETRIEVAL; RISK ANALYSIS; RISK MANAGEMENT; SUPPORT VECTOR MACHINES; VECTORS;

RISK ASSESSMENT;

EID: 70349437327     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.05.024     Document Type: Article

References (20)

1. Baesens, B. (2003). Developing intelligent systems for credit scoring using maching learning techniques. PhD thesis, Katholieke Universiteit Leuven.
2. Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
3. Breiman L. Prediction games and arcing algorithms. Neural Computation 11 7 (1999) 1493-1517
4. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 121-167
5. Chang, C.-C., & Lin, C.-J. (2001). Libsvm: A library for support vector machines. Available from: .
6. Coelho, A. L. V., Lima, C. A. M., & Von Zuben, F. J. (2003). Ga-Based selection of components for heterogeneous ensembles of support vector machines. In Proceedings of IEEE international conference on evolutionary computation. Canberra, Australia (pp. 2238-2245).
7. Franc, V., & Hlavac, V. (2004). Statistical pattern recognition toolbox for matlab. Available from: .
8. Hansen L.K., and Salamon P. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 993-1001
9. HSN Consultants Inc. (2007). The Nilson report. Oxnard, California.
10. Huang C.-L., Chen M.-C., and Wang C.-J. Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications 33 4 (2007) 847-856
11. Kim H.-C., Pang S.N., Je H.-M., et al. Constructing support vector machine ensemble. Pattern Recognition 36 (2003) 2757-2767
12. Maimon O., and Rokach L. Data mining and knowledge discovery handbook (2005), Springer, New York
13. Ma J.S., Krishnamurthy A., and Ahalt S. Svm training with duplicated samples and its application in Svm-based ensemble methods. Neurocomputing 61 (2004) 455-459
14. Scherrer B. Biostatistique (1984), Gaëtan Morin, Chicoutimi
15. Sun, B. Y., & Huang, D. S. (2004). Least squares support vector machine ensemble. In Proceedings 2004 IEEE international joint conference on neural networks (pp. 2013-2016).
16. Suykens J.A.K., Gestel T.V., Brabanter J.D., Moor B.D., et al. Least squares support vector machines (2002), World Scientific, Siningapore
17. Thomas L.C., Edelman D.B., and Crook J.N. Credit scoring and its applications (2002), Siam, Philadelphia
18. Thomas L.C., Oliver R.W., and Hand D.J. A survey of the issues in consumer credit modelling research. Journal of the Operational Research Society 56 9 (2005) 1006-1015
19. Vezhnevets, A., & Vezhnevets, V. (2005). 'Modest Adaboost' - Teaching Adaboost to generalize better. In Graphicon-2005. Novosibirsk Akademgorodok, Russia.
20. Yu L., Lai K.K., Wang S.Y., and Huang W. A bias-variance-complexity trade-off framework for complex system modeling. Lecture Notes in Computer Science 3980 (2006) 518-527