Browse > Article
http://dx.doi.org/10.3745/JIPS.04.0154

A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set  

Xu, Wei (School of Business, Jiangnan University)
Yang, Daoli (School of Business Planning, Chongqing Technology and Business University)
Publication Information
Journal of Information Processing Systems / v.15, no.6, 2019 , pp. 1489-1502 More about this Journal
Abstract
This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.
Keywords
Business Failure Prediction; Combination Method; Different Sample Sizes; Soft Set; uni-int Decision Making Method;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
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