Name
Abstract | ||
|---|---|---|
With the recent financial crisis and European debt crisis, corporate bankruptcy prediction has become an increasingly important issue for financial institutions. Many statistical and intelligent methods have been proposed, however, there is no overall best method has been used in predicting corporate bankruptcy. Recent studies suggest ensemble learning methods may have potential applicability in corporate bankruptcy prediction. In this paper, a new and improved Boosting, FS-Boosting, is proposed to predict corporate bankruptcy. Through injecting feature selection strategy into Boosting, FS-Booting can get better performance as base learners in FS-Boosting could get more accuracy and diversity. For the testing and illustration purposes, two real world bankruptcy datasets were selected to demonstrate the effectiveness and feasibility of FS-Boosting. Experimental results reveal that FS-Boosting could be used as an alternative method for the corporate bankruptcy prediction. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.eswa.2013.09.033 | Expert Syst. Appl. |
Keywords | Field | DocType |
feature selection,financial institution,improved boosting,corporate bankruptcy prediction,intelligent method,corporate bankruptcy,alternative method,european debt crisis,overall best method,real world bankruptcy datasets,recent financial crisis,ensemble learning,boosting | Data mining,Actuarial science,Feature selection,Computer science,Financial crisis,European debt crisis,Bankruptcy prediction,Boosting (machine learning),Bankruptcy,Ensemble learning | Journal |
Volume | Issue | ISSN |
41 | 5 | 0957-4174 |
Citations | PageRank | References |
9 | 0.46 | 33 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Wang | 1 | 200 | 7.20 |
| Jian Ma | 2 | 1662 | 103.00 |
| Shanlin Yang | 3 | 787 | 60.80 |