A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description
Year of publication: |
2022
|
---|---|
Authors: | Yuan, Kunpeng ; Chi, Guotai ; Zhou, Ying ; Yin, Hailei |
Published in: |
Research in international business and finance. - Amsterdam [u.a.] : Elsevier, ISSN 0275-5319, ZDB-ID 424514-3. - Vol. 59.2022, p. 1-24
|
Subject: | Big data | Default prediction | K-means clustering | Optimal cluster number | Optimal kernel function | Support vector domain description | Prognoseverfahren | Forecasting model | Clusteranalyse | Cluster analysis | Big Data | Data Mining | Data mining | Regionales Cluster | Regional cluster | Kreditrisiko | Credit risk | Theorie | Theory | Mustererkennung | Pattern recognition |
-
Depth-based support vector classifiers to detect data nests of rare events
Dyckerhoff, Rainer, (2021)
-
Applications of data analytics : cluster analysis of not-for-profit data
Alzamil, Zamil S., (2021)
-
A big data analysis system for financial trading
Huang, Shian-Chang, (2017)
- More ...
-
Feature selection in credit risk modeling : an international evidence
Zhou, Ying, (2021)
-
Chi, Guotai, (2020)
-
Modeling credit risk with a multi-stage hybrid model : an alternative statistical approach
Uddin, Mohammad S., (2022)
- More ...