Name
Title | ||
|---|---|---|
A hybrid approach of DEA, rough set and support vector machines for business failure prediction |
Abstract | ||
|---|---|---|
The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. While the efficiency of a corporation's management is generally acknowledged to be a key contributor to corporation's bankrupt, it is usually excluded from early prediction models. The objective of the study is to use efficiency as predictive variables with a proposed novel model to integrate rough set theory (RST) with support vector machines (SVM) technique to increase the accuracy of the prediction of business failure. In the proposed method (RST-SVM), data envelopment analysis (DEA) is employed as a tool to evaluate the input/output efficiency. Furthermore, by RST approach, the redundant attributes in multi-attribute information table can be reduced, which showed that the number of independent variables was reduced with no information loss, is utilized as a preprocessor to improve business failure prediction capability by SVM. The effectiveness of the methodology was verified by experiments comparing back-propagation neural networks (BPN) approach with the hybrid approach (RST-BPN). The results shows that DEA do provide valuable information in business failure predictions and the proposed RST-SVM model provides better classification results than RST-BPN model, no matter when only considering financial ratios or the model including both financial ratios and DEA. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2009.06.088 | Expert Syst. Appl. |
Keywords | Field | DocType |
dea,support vector machine,rough set,business failure prediction,business failure,support vector machines,business failure prediction capability,proposed rst-svm model,early prediction model,financial ratios,financial ratio,hybrid approach,rst approach,proposed novel model,rst-bpn model,input output,rough set theory,neural networks,data envelope analysis,feature selection,efficiency,prediction model,data envelopment analysis,discriminant analysis | Financial ratio,Data mining,Feature selection,Computer science,Support vector machine,Rough set,Bankruptcy prediction,Data envelopment analysis,Artificial intelligence,Variables,Business failure,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 2 | Expert Systems With Applications |
Citations | PageRank | References |
49 | 1.28 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ching-Chiang Yeh | 1 | 160 | 8.87 |
| Der-Jang Chi | 2 | 67 | 3.70 |
| Ming-Fu Hsu | 3 | 124 | 11.40 |