1 |
W. Xu and Z. Xiao, "Soft set theory oriented forecast combination method for business failure prediction," Journal of Information Processing Systems, vol. 12, no. 1, pp. 109-128, 2016.
DOI
|
2 |
X. Zhao, K. Yeung, Q. Huang, and X. Song, "Improving the predictability of business failure of supply chain finance clients by using external big dataset," Industrial Management & Data Systems, vol. 115, no. 9, pp. 1683-1703, 2015.
DOI
|
3 |
W. Xu, Y. Pan, W. Chen, and H. Fu, "Forecasting corporate failure in the Chinese energy sector: a novel integrated model of deep learning and support vector machine," Energies, vol. 12, no. 12, article no. 2251, 2019.
|
4 |
E. I. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," Journal of Finance, vol. 23, no. 4, pp. 589-609, 1968.
DOI
|
5 |
J. A. Ohlson, "Financial ratios and the probabilistic prediction of bankruptcy," Journal of Accounting Research, vol. 18, no. 1, pp. 109-131, 1980.
DOI
|
6 |
L. Wang and C. Wu, "Business failure prediction based on two-stage selective ensemble with manifold learning algorithm and kernel-based fuzzy self-organizing map," Knowledge-Based Systems, vol. 121, pp. 99-110, 2017.
DOI
|
7 |
H. Li, J. Sun, and B. L. Sun, "Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors," Expert Systems with Applications, vol. 36, no. 1, pp. 643-659, 2009.
DOI
|
8 |
D. Wang, X. Song, W. Yin, and J. Yuan, "Forecasting core business transformation risk using the optimal rough set and the neural network," Journal of Forecasting, vol. 34, no. 6, pp. 478-491, 2015.
DOI
|
9 |
Y. Zelenkov, E. Fedorova, and D. Chekrizov, "Two-step classification method based on genetic algorithm for bankruptcy forecasting," Expert Systems with Applications, vol. 88, pp. 393-401, 2017.
DOI
|
10 |
L. Zhou, Y. W. Si, and H. Fujita, "Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method," Knowledge-Based Systems, vol. 128, pp. 93-101, 2017.
DOI
|
11 |
J. Sun, H. Fujita, P. Chen, and H. Li, "Dynamic financial distress prediction with concept drift based on time weighting combined with AdaBoost support vector machine ensemble," Knowledge-Based Systems, vol. 120, pp. 4-14, 2017.
DOI
|
12 |
J. M. Bates and C. W. Granger, "The combination of forecasts," Journal of the Operational Research Society, vol. 20, no. 4, pp. 451-468, 1969.
DOI
|
13 |
Z. Xiao, X. Yang, Y. Pang, and X. Dang, "The prediction for listed companies' financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory," Knowledge-Based Systems, vol. 26, pp. 196-206, 2012.
DOI
|
14 |
D. Molodtsov, "Soft set theory: first results," Computers & Mathematics with Applications, vol. 37, no. 4-5, pp. 19-31, 1999.
DOI
|
15 |
F. Feng, Y. Li, and N. Cagman, "Generalized uni-int decision making schemes based on choice value soft sets," European Journal of Operational Research, vol. 220, no. 1, pp. 162-170, 2012.
DOI
|
16 |
H. Li, Y. H. Xu, and L. Yu, "Predicting hospitality firm failure: mixed sample modelling," International Journal of Contemporary Hospitality Management, vol. 29, no. 7, pp. 1770-1792, 2017.
DOI
|
17 |
A. I. Dimitras, S. H. Zanakis, and C. Zopounidis, "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, vol. 90, no. 3, pp. 487-513, 1996.
DOI
|
18 |
N. Cagman and S. Enginoglu, "Soft set theory and uni-int decision making," European Journal of Operational Research, vol. 207, no. 2, pp. 848-855, 2010.
DOI
|
19 |
F. Feng, J. Cho, W. Pedrycz, H. Fujita, and T. Herawan, "Soft set based association rule mining," Knowledge-Based Systems, vol. 111, pp. 268-282, 2016.
DOI
|
20 |
S. Enginoglu, S. Memis, and B. Arslan, "Comment (2) on soft set theory and uni-int decision-making [European Journal of Operational Research, (2010) 207, 848-855]," Journal of New Theory, vol. 2018, no. 25, pp. 84-102, 2018.
|
21 |
J. H. Min and Y. C. Lee, "Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters," Expert Systems with Applications, vol. 28, no. 4, pp. 603-614, 2005.
DOI
|
22 |
A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997.
DOI
|
23 |
F. Lin, C. C. Yeh, and M. Y. Lee, "The use of hybrid manifold learning and support vector machines in the prediction of business failure," Knowledge-Based Systems, vol. 24, no. 1, pp. 95-101, 2011.
DOI
|
24 |
P. Ravisankar, V. Ravi, and I. Bose, "Failure prediction of dotcom companies using neural network-genetic programming hybrids," Information Sciences, vol. 180, no. 8, pp. 1257-1267, 2010.
DOI
|
25 |
W. Xu, Z. Xiao, X. Dang, D. Yang, and X. Yang, "Financial ratio selection for business failure prediction using soft set theory," Knowledge-Based Systems, vol. 63, pp. 59-67 2014.
DOI
|