Open Access
Issue
MATEC Web Conf.
Volume 76, 2016
20th International Conference on Circuits, Systems, Communications and Computers (CSCC 2016)
Article Number 02039
Number of page(s) 9
Section Systems
DOI https://doi.org/10.1051/matecconf/20167602039
Published online 21 October 2016
  1. 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]
  2. E. I. Altman, “Financial ratios, discriminant analysis and the prediction of corporate bankruptcy,”Journal of Finance, vol. 23, pp. 89–609, 1968
  3. 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]
  4. 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.
  5. Viganò, L. A. (1993). Credit scoring model for development banks: An African case study. Savings and Development, 17(4), 441–482.
  6. 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]
  7. 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]
  8. 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]
  9. 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]
  10. 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. [CrossRef]
  11. 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]
  12. 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]
  13. 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. [CrossRef]
  14. 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
  15. 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]
  16. 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. [CrossRef]
  17. 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]
  18. 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.
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. Vaclav Kozeny, Genetic algorithms for credit scoring: Alternative fitness function performance comparison” Expert Systems with Applications, vol. 42, pp. 2998–3004, 2015. [CrossRef]
  25. 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]
  26. 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]
  27. S. Piramuthu, “Financial credit-risk evaluation with neural and neurofuzzy systems,” European Journal of Operational Research, vol. 112(2), pp. 310–321, 1999 [CrossRef]
  28. 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.
  29. 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. [CrossRef]
  30. 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]
  31. 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]
  32. Lean Yu, 2014, Credit Risk Evaluation with a Least Squares Fuzzy Support Vector Machines Classifier
  33. Terry Harris, Credit scoring using the clustered support vector machine, Expert Systems with Applications, 2015.
  34. S. Haykin, Neural Networks: A comprehensive foundation, Prentice Hall, 1998.
  35. 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.
  36. 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
  37. 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]
  38. Abdi., H., & Williams, L.J. (2010). “Principal component analysis.”. Wiley Interdisciplinary Reviews: Computational Statistics, 2: 433–459. [CrossRef]
  39. T. Jolliffee, Principal Component Analysis, Springer Secaucus, NJ,USA,2002.
  40. Hussein A. Abdou1,* and John Pointon, credit scoring, statistical techniques and evaluation criteria: a review of the literature
  41. RumelhartD.E. G.E. Hinton, R.J. Williams Learning internal representation by error propagation Parallel Distrib. Process., 1 (1986), pp. 318–362

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.