Name
Abstract | ||
|---|---|---|
•This paper examines the construction issues of classifier ensembles for bankruptcy prediction.•The first issue focuses on the classification techniques, which are based on MLP, SVM, and DT.•The second issue is the combination method, which is based on bagging and boosting.•The third issue is based on examining different numbers of combined classifiers.•We show that DT ensembles composed of 80–100 classifiers using the boosting method perform best. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.asoc.2014.08.047 | Applied Soft Computing |
Keywords | Field | DocType |
Bankruptcy prediction,Credit scoring,Classifier ensembles,Data mining,Machine learning | Pattern recognition,Random subspace method,Computer science,Support vector machine,Cascading classifiers,Bankruptcy prediction,Boosting (machine learning),Artificial intelligence,Classifier (linguistics),Margin classifier,Probabilistic classification,Machine learning | Journal |
Volume | ISSN | Citations |
24 | 1568-4946 | 18 |
PageRank | References | Authors |
0.56 | 27 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-fong Tsai | 1 | 1255 | 54.93 |
| Yu-Feng Hsu | 2 | 258 | 17.15 |
| David C. Yen | 3 | 2292 | 143.11 |