- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 564213, 9 pages
Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier
1Alibaba Business College, Hangzhou Normal University, Hangzhou 310036, China
2School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
Received 12 February 2014; Accepted 27 May 2014; Published 12 June 2014
Academic Editor: Fenghua Wen
Copyright © 2014 Lean Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- E. I. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,” Journal of Finance, vol. 23, pp. 89–609, 1968.
- J. C. Wiginton, “A note on the comparison of logit and discriminant models of consumer credit behaviour,” Journal of Financial Quantitative Analysis, vol. 15, pp. 757–770, 1980.
- B. J. Grablowsky and W. K. Talley, “Probit and discriminant functions for classifying credit applicants: a comparison,” Journal of Economic Business, vol. 33, pp. 254–261, 1981.
- F. Glover, “Improved linear programming models for discriminant analysis,” Decision Science, vol. 21, pp. 771–785, 1990.
- O. L. Mangasarian, “Linear and nonlinear separation of patterns by linear programming,” Operations Research, vol. 13, pp. 444–452, 1965.
- W. E. Henley and D. J. Hand, “A k-nearest-neighbour classifier for assessing consumer credit risk,” Journal of the Royal Statistical Society Series D: The Statistician, vol. 45, no. 1, pp. 77–95, 1996.
- P. Makowski, “Credit scoring branches out,” Credit World, vol. 75, pp. 30–37, 1985.
- K. K. Lai, L. Yu, S. Y. Wang, and L. G. Zhou, “Neural network meta-learning for credit scoring,” in Intelligent Computing, vol. 4113 of Lecture Notes in Computer Science, pp. 403–408, 2006.
- R. Malhotra and D. K. Malhotra, “Evaluating consumer loans using neural networks,” Omega, vol. 31, no. 2, pp. 83–96, 2003.
- M.-C. Chen and S.-H. Huang, “Credit scoring and rejected instances reassigning through evolutionary computation techniques,” Expert Systems with Applications, vol. 24, no. 4, pp. 433–441, 2003.
- F. Varetto, “Genetic algorithms applications in the analysis of insolvency risk,” Journal of Banking and Finance, vol. 22, no. 10-11, pp. 1421–1439, 1998.
- Z. Zhu, H. He, J. A. Starzyk, and C. Tseng, “Self-organizing learning array and its application to economic and financial problems,” Information Sciences, vol. 177, no. 5, pp. 1180–1192, 2007.
- Z. Huang, H. Chen, C.-J. Hsu, W.-H. Chen, and S. Wu, “Credit rating analysis with support vector machines and neural networks: A Market Comparative Study,” Decision Support Systems, vol. 37, no. 4, pp. 543–558, 2004.
- K. K. Lai, L. Yu, L. G. Zhou, and S. Y. Wang, “Credit risk evaluation with least square support vector machine,” in Rough Sets and Knowledge Technology, vol. 4062 of Lecture Notes in Artificial Intelligence, pp. 490–495, 2006.
- K. K. Lai, L. Yu, W. Huang, and S. Y. Wang, “A novel support vector machine metamodel for business risk identification,” in PRICAI 2006: Trends in Artificial Intelligence, vol. 4099 of Lecture Notes in Artificial Intelligence, pp. 480–484, 2006.
- R. Malhotra and D. K. Malhotra, “Differentiating between good credits and bad credits using neuro-fuzzy systems,” European Journal of Operational Research, vol. 136, no. 1, pp. 190–211, 2002.
- L. Zhou, K. K. Lai, and L. Yu, “Least squares support vector machines ensemble models for credit scoring,” Expert Systems with Applications, vol. 37, no. 1, pp. 127–133, 2010.
- L. Zhou, K. K. Lai, and L. Yu, “Credit scoring using support vector machines with direct search for parameters selection,” Soft Computing, vol. 13, no. 2, pp. 149–155, 2009.
- L. Yu and X. Yao, “A total least squares proximal support vector classifier for credit risk evaluation,” Soft Computing, vol. 17, no. 4, pp. 643–650, 2013.
- L. Yu, X. Yao, S. Wang, and K. K. Lai, “Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection,” Expert Systems with Applications, vol. 38, no. 12, pp. 15392–15399, 2011.
- L. Yu, S. Wang, and J. Cao, “A modified least squares support vector machine classifier with application to credit risk analysis,” International Journal of Information Technology and Decision Making, vol. 8, no. 4, pp. 697–710, 2009.
- L. Yu, S. Wang, and K. K. Lai, “Credit risk evaluation using a c-variable least squares support vector classification model,” Communications in Computer and Information Science, vol. 35, pp. 573–579, 2009.
- T.-S. Lee, C.-C. Chiu, C.-J. Lu, and I.-F. Chen, “Credit scoring using the hybrid neural discriminant technique,” Expert Systems with Applications, vol. 23, no. 3, pp. 245–254, 2002.
- S. Piramuthu, “Financial credit-risk evaluation with neural and neurofuzzy systems,” European Journal of Operational Research, vol. 112, no. 2, pp. 310–321, 1999.
- Y. Wang, S. Wang, and K. K. Lai, “A new fuzzy support vector machine to evaluate credit risk,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 6, pp. 820–831, 2005.
- L. Yu, S. Wang, F. Wen, K. K. Lai, and S. He, “Designing a hybrid intelligent mining system for credit risk evaluation,” Journal of Systems Science & Complexity, vol. 21, no. 4, pp. 527–539, 2008.
- R. Smalz and M. Conrad, “Combining evolution with credit apportionment: a new learning algorithm for neural nets,” Neural Networks, vol. 7, no. 2, pp. 341–351, 1994.
- K. K. Lai, L. Yu, S. Y. Wang, and L. G. Zhou, “Credit risk analysis using a reliability-based neural network ensemble model,” in Proceedings of the International Conference on Artificial Neural Networks (ICANN '06), vol. 4132 of Lecture Notes in Computer Science, pp. 682–690, 2006.
- L. Yu, S. Wang, and K. K. Lai, “Credit risk assessment with a multistage neural network ensemble learning approach,” Expert Systems with Applications, vol. 34, no. 2, pp. 1434–1444, 2008.
- L. Yu, W. Yue, S. Wang, and K. K. Lai, “Support vector machine based multiagent ensemble learning for credit risk evaluation,” Expert Systems with Applications, vol. 37, no. 2, pp. 1351–1360, 2010.
- L. Yu, S. Wang, and K. K. Lai, “An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring,” European Journal of Operational Research, vol. 195, no. 3, pp. 942–959, 2009.
- L. C. Thomas, “A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers,” International Journal of Forecasting, vol. 16, no. 2, pp. 149–172, 2000.
- L. C. Thomas, R. W. Oliver, and D. J. Hand, “A survey of the issues in consumer credit modelling research,” Journal of the Operational Research Society, vol. 56, no. 9, pp. 1006–1015, 2005.
- L. Yu, S. Y. Wang, K. K. Lai, and L. G. Zhou, Bio-Inspired Credit Risk Analysis—Computational Intelligence with Support Vector Machines, Springer, Berlin, Germany, 2008.
- C.-F. Lin and S.-D. Wang, “Fuzzy support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 464–471, 2002.
- D. Tsujinishi and S. Abe, “Fuzzy least squares support vector machines for multiclass problems,” Neural Networks, vol. 16, no. 5-6, pp. 785–792, 2003.
- V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
- R. Fletcher, Practical Methods of Optimization, A Wiley-Interscience Publication, John Wiley & Sons, 2nd edition, 1987.
- J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, no. 3, pp. 293–300, 1999.
- L. C. Thomas, D. B. Edelman, and J. N. Crook, Credit Scoring and Its Applications, Society of Industrial and Applied Mathematics, Philadelphia, Pa, USA, 2002.
- M.-C. Chen, L.-S. Chen, C.-C. Hsu, and W.-R. Zeng, “An information granulation based data mining approach for classifying imbalanced data,” Information Sciences, vol. 178, no. 16, pp. 3214–3227, 2008.
- A. Celikyilmaz and I. B. Turksen, “Fuzzy functions with support vector machines,” Information Sciences, vol. 177, no. 23, pp. 5163–5177, 2007.
- L. Cao and F. E. H. Tay, “Financial forecasting using Support Vector Machines,” Neural Computing and Applications, vol. 10, no. 2, pp. 184–192, 2001.
- T. Van Gestel, B. Baesens, J. Garcia, and P. Van Dijcke, “A support vector machine approach to credit scoring,” Bank en Financiewezen, vol. 2, pp. 73–82, 2003.
- Q. McNemar, “Note on the sampling error of the difference between correlated proportions or percentages,” Psychometrika, vol. 12, no. 2, pp. 153–157, 1947.
- D. R. Cooper and C. W. Emory, Business Research Methods, Irwin, Chicago, Ill, USA, 1995.