MATEC Web of Conferences
Volume 54, 20162016 7th International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2016)
|Number of page(s)||5|
|Section||Computer information science and Its Applications|
|Published online||22 April 2016|
- E. I. Altman and A. Saunders, “Credit risk measurement: developments over the last 20 years,” Journal of Banking and Finance, vol. 21, no. 11-12, pp. 1721–1742, (1997) [CrossRef]
- Z. Davoodabadi and A. Moeini, “Building Customers’ Credit Scoring Models with Combination of Feature Selection and Decision Tree Algorithms,” vol. 4, no. 2, pp. 97–103, (2015)
- A. Khashman, “A neural network model for credit risk evaluation,” International Journal of Neural Systems, vol. 19, no. 4, pp.285–294, (2009) [CrossRef]
- T. Bellotti and J. Crook, “Support vector machines for credit scoring and discovery of significant features,” Expert Systems with Applications, vol. 36, no. 2, pp. 3302–3308, (2009) [CrossRef]
- F. Wen and X. Yang, “Skewness of return distribution and coefficient of risk premium,” Journal of Systems Science and Complexity, vol. 22, no. 3, pp. 360–371, (2009) [CrossRef]
- X. Zhou, W. Jiang, Y. Shi, and Y. Tian, “Credit risk evaluation with kernel-based affine subspace nearest points learning method,” Expert Systems with Applications, vol. 38, no. 4, pp.4272–4279, (2011) [CrossRef]
- G. Kim, C. Wu, S. Lim, and J. Kim, “Modified matrix splitting method for the support vector machine and its application to the credit classification of companies in Korea,” Expert Systems with Applications, vol. 39, no. 10, pp. 8824–8834, (2012) [CrossRef]
- Liu, and Motoda, “Feature selection for Knowledge Discovery and Data mining,” Kluwer Academic Publishers, 1998.
- Guyon, and Elisseeff, “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, pp 1157–1182, 2003.
- OreskiS., OreskiD., & OreskiG., “Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment”. Expert Systems with Applications, 39(16), 12605–12617, 2012 [CrossRef]
- SaberiM., MirtalaieM. S., HussainF. K., AzadehA., HussainO. K., & AshjariB. “A granular computing-based approach to credit scoring modeling”. Neurocomputing, 122, 100–115, (2013) [CrossRef]
- S. Lee, & W. S. Choi, “A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis”. Expert Systems with Applications, 40(8), 2941–2946, (2013) [CrossRef]
- GhatgeA. R., & Halkarnikar., “Ensemble Neural Network Strategy for Predicting Credit Default Evaluation”, 2(7), 223–225, (2013)
- ChaudhuriA., & DeK., “Fuzzy Support Vector Machine for bankruptcy prediction. Applied Soft Computing Journal”, 11(2), 2472–2486, (2011)
- GhodselahiA., “A Hybrid Support Vector Machine Ensemble Model for Credit Scoring,” International Journal of Computer Applications, 17(5), 1–5, (2011) [CrossRef]
- Huang L., Chen C., and Wang J., “Credit Scoring with a Data Mining Approach Based on Support Vector Machines,” Computer Journal of Expert Systems with Applications, vol. 33, no. 4, pp. 847–856, (2007) [CrossRef]
- G. Eason, B. Li T., Shiue W., and Huang H., “The Evaluation of Consumer Loans Using Support Vector Machines,” Computer Journal of Expert Systems with Applications, vol. 30, no. 4, pp. 772–782, (2006) [CrossRef]
- MartensD., BaesensB., GestelT., and VanthienenJ., “Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines,” European Computer Journal of Operational Research, vol. 183, no. 3, pp. 1466–1476, (2007) [CrossRef]
- Y. Wang, S. Wang, and K. Lai, “A New Fuzzy Support Vector Machine to Evaluate Credit Risk,” Computer Journal of IEEE Transactions on Fuzzy Systems, vol. 13, no. 6, pp. 25–29, (2005)
- S. Oreski and G. Oreski, “Genetic algorithm-based heuristic for feature selection in credit risk assessment,” Expert System. Appl., vol. 41, no. 4, pp. 2052–2064, (2014) [CrossRef]
- Y. Ling, Q. Y. Cao, and H. Zhang, “Application of the PSO-SVM model for credit scoring,” Proc. - 2011 7th Int. Conf. Comput. Intell. Secur. CIS 2011, pp. 47–51, (2011)
- Deron Liang, Chih-Fong Tsai, Hsin-Ting Wua, “The effect of feature selection on financial distress prediction,” Knowledge-Based Systems 73 289–297 (2015) [CrossRef]
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