Name
Abstract | ||
|---|---|---|
•A number of Mahalanobis stationary kernels were proposed for credit risk evaluation.•The Mahalanobis kernels are suitable for SVM framework, with a theoretical illustration.•Stationary kernels with Mahalanobis distance outperform the stationary kernels with various distance measures, describing the behavior of datasets more properly.•The proposed kernels can outperform state-of-the-art models in credit risk evaluations, generating more robust models in terms of specificity and sensitivity.•The satisfying performance of the proposed kernels in credit risk evaluation may provide potential options in risk modeling. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2018.07.005 | Applied Soft Computing |
Keywords | Field | DocType |
Mahalanobis distance,Support Vector Machine (SVM),Indefinite,Stationary kernel,Credit risk | Kernel (linear algebra),Data set,Support vector machine,Mahalanobis distance,Correlation,Artificial intelligence,Machine learning,Credit risk,Mathematics,Distance measures | Journal |
Volume | ISSN | Citations |
71 | 1568-4946 | 4 |
PageRank | References | Authors |
0.38 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Jiang | 1 | 22 | 8.41 |
| Wai-Ki Ching | 2 | 683 | 78.66 |
| Ka Fai Cedric Yiu | 3 | 176 | 23.70 |
| Yushan Qiu | 4 | 20 | 6.28 |