Open Access
Issue |
MATEC Web Conf.
Volume 76, 2016
20th International Conference on Circuits, Systems, Communications and Computers (CSCC 2016)
|
|
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Article Number | 02039 | |
Number of page(s) | 9 | |
Section | Systems | |
DOI | https://doi.org/10.1051/matecconf/20167602039 | |
Published online | 21 October 2016 |
- Lyn C. Thomas, A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers, International Journal of Forcasting, vol. 16, issue 2, pp. 149–172, April-June 2000. [CrossRef] [Google Scholar]
- E. I. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,”Journal of Finance, vol. 23, pp. 89–609, 1968 [Google Scholar]
- Kleimeier, S., & Dinh, T. A. (2007). Credit scoring model for Vietnam’s retail banking market. International Review of Financial Analysis, 16(5), 471–495. [CrossRef] [Google Scholar]
- Rayo, S., Lara, J., & Camino, D. (2010). A credit scoring model for institutions of microfinance under the Basel II normative. Journal of Economics, Finance & Administrative Science, 15(28), 89–124. [Google Scholar]
- Viganò, L. A. (1993). Credit scoring model for development banks: An African case study. Savings and Development, 17(4), 441–482. [Google Scholar]
- Vogelgesang, U. (2003). Microfinance in times of crisis: The effects of competition, rising indebtness, and economic crisis on repayment behaviour. World Development, 31(12), 2085–2114. [CrossRef] [Google Scholar]
- Lee, T. S., & Chen, I. F. (2005). A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28(4), 743–752. [CrossRef] [Google Scholar]
- Yu, L., Wang, S. A. and Lai, K. K. 2008. Credit risk assessment with a multistage neural network ensemble learning approach. Expert systems with applications. vol. 34. pp. 1434–1444. [CrossRef] [Google Scholar]
- Tsai, C.-f. and Wu, J.-w. 2008. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert systems with applications. vol. 34. pp. 2639–2649. [CrossRef] [Google Scholar]
- Angelini, E., Tollo, G. D. and Roil, A. 2008. A neural network approach for credit risk evaluation. The quarterly review of economics and finance. vol. 48. pp. 733–755. [Google Scholar]
- Antonio Blanco, Rafael Pino-Mejías, Juan Lara, Rayo Salvador, Credit scoring models for the microfinance industry using neural networks:Evidence from Peru, Expert Systems with Applications 40 (2013) 356–364 [CrossRef] [Google Scholar]
- ZongyuanZhao a, ShuxiangXu a, ByeongHo Kang b, Mir Md JahangirKabir a, YunlingLiu, Rainer Wasinger a Investigation and improvement of multi-layer perceptron neural networks for credit scoring, Expert Systems with Applications 42 (2015) 3508–3516 [CrossRef] [Google Scholar]
- 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. [Google Scholar]
- K. K. Lai, L. Yu, S. Y. Wang, and L. G. Zhou, “Neural network meta-learning for credit scoring,” inIntelligent Computing, vol. 4113 of Lecture Notes in Computer Science, pp. 403–408, 2006. View at Google Scholar [Google Scholar]
- R. Malhotra and D. K. Malhotra, “Evaluating consumer loans using neural networks,” Omega, vol. 31, no. 2, pp. 83–96, 2003. View at Publisher · View at Google Scholar · View at Scopus [CrossRef] [Google Scholar]
- 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. [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- 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. [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- Vaclav Kozeny, Genetic algorithms for credit scoring: Alternative fitness function performance comparison” Expert Systems with Applications, vol. 42, pp. 2998–3004, 2015. [CrossRef] [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- Malhotra, R., & Malhotra, D. K. (2002). Differentiating between good credits and bad credits using neuro-fuzzy systems. European Journal of Operational Research, 136(1), 190–211 [CrossRef] [Google Scholar]
- S. Piramuthu, “Financial credit-risk evaluation with neural and neurofuzzy systems,” European Journal of Operational Research, vol. 112(2), pp. 310–321, 1999 [CrossRef] [Google Scholar]
- 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. [Google Scholar]
- 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. [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- 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. [CrossRef] [Google Scholar]
- Lean Yu, 2014, Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier [Google Scholar]
- Terry Harris, Credit scoring using the clustered support vector machine, Expert Systems with Applications, 2015. [Google Scholar]
- S. Haykin, Neural Networks: A comprehensive foundation, Prentice Hall, 1998. [Google Scholar]
- L. Yu, J.-h. Zhu, L.-j. Chen Parametric study on PCA-based algorithm for structural health Prognostics & System Health Management Conference, Macao (2010), pp. 1–6. [Google Scholar]
- M.A. Rassam, A. Zainal, M.A. Maarof An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications Applied Soft Computing, 13 (2012), pp. 1978–1996 [Google Scholar]
- Jie Wang, Jun Wang, Forecasting stock market indexes using principal component analysis and stochastic time effective neural networks, Neurocomputing , Volume 156, 25 May 2015, Pages 68–78 [CrossRef] [Google Scholar]
- Abdi., H., & Williams, L.J. (2010). “Principal component analysis.”. Wiley Interdisciplinary Reviews: Computational Statistics, 2: 433–459. [CrossRef] [Google Scholar]
- T. Jolliffee, Principal Component Analysis, Springer Secaucus, NJ,USA,2002. [Google Scholar]
- Hussein A. Abdou1,* and John Pointon, credit scoring, statistical techniques and evaluation criteria: a review of the literature [Google Scholar]
- RumelhartD.E. G.E. Hinton, R.J. Williams Learning internal representation by error propagation Parallel Distrib. Process., 1 (1986), pp. 318–362 [Google Scholar]
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