Open Access
Issue |
MATEC Web of Conferences
Volume 54, 2016
2016 7th International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2016)
|
|
---|---|---|
Article Number | 05004 | |
Number of page(s) | 5 | |
Section | Computer information science and Its Applications | |
DOI | https://doi.org/10.1051/matecconf/20165405004 | |
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) [Google Scholar]
- 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) [Google Scholar]
- A. Khashman, “A neural network model for credit risk evaluation,” International Journal of Neural Systems, vol. 19, no. 4, pp.285–294, (2009) [CrossRef] [Google Scholar]
- 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] [Google Scholar]
- 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] [Google Scholar]
- 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] [Google Scholar]
- 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] [Google Scholar]
- Liu, and Motoda, “Feature selection for Knowledge Discovery and Data mining,” Kluwer Academic Publishers, 1998. [Google Scholar]
- Guyon, and Elisseeff, “An Introduction to Variable and Feature Selection,” Journal of Machine Learning Research, pp 1157–1182, 2003. [Google Scholar]
- 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] [Google Scholar]
- SaberiM., MirtalaieM. S., HussainF. K., AzadehA., HussainO. K., & AshjariB. “A granular computing-based approach to credit scoring modeling”. Neurocomputing, 122, 100–115, (2013) [CrossRef] [Google Scholar]
- 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] [Google Scholar]
- GhatgeA. R., & Halkarnikar., “Ensemble Neural Network Strategy for Predicting Credit Default Evaluation”, 2(7), 223–225, (2013) [Google Scholar]
- ChaudhuriA., & DeK., “Fuzzy Support Vector Machine for bankruptcy prediction. Applied Soft Computing Journal”, 11(2), 2472–2486, (2011) [Google Scholar]
- GhodselahiA., “A Hybrid Support Vector Machine Ensemble Model for Credit Scoring,” International Journal of Computer Applications, 17(5), 1–5, (2011) [CrossRef] [Google Scholar]
- 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] [Google Scholar]
- 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] [Google Scholar]
- 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] [Google Scholar]
- 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) [Google Scholar]
- 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] [Google Scholar]
- 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) [Google Scholar]
- Deron Liang, Chih-Fong Tsai, Hsin-Ting Wua, “The effect of feature selection on financial distress prediction,” Knowledge-Based Systems 73 289–297 (2015) [Google Scholar]
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.