Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination
Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720903631 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849697778069929984 |
|---|---|
| author | Ying Liu Lihua Huang |
| author_facet | Ying Liu Lihua Huang |
| author_sort | Ying Liu |
| collection | DOAJ |
| description | Recently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China’s listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach. |
| format | Article |
| id | doaj-art-71b7b0e91ebe4b0ea901ece3b9c46f98 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-71b7b0e91ebe4b0ea901ece3b9c46f982025-08-20T03:19:07ZengWileyInternational Journal of Distributed Sensor Networks1550-14772020-01-011610.1177/1550147720903631Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise eliminationYing Liu0Lihua Huang1School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaRecently, support vector machines, a supervised learning algorithm, have been widely used in the scope of credit risk management. However, noise may increase the complexity of the algorithm building and destroy the performance of classifier. In our work, we propose an ensemble support vector machine model to solve the risk assessment of supply chain finance, combined with reducing noises method. The main characteristics of this approach include that (1) a novel noise filtering scheme that avoids the noisy examples based on fuzzy clustering and principal component analysis algorithm is proposed to remove both attribute noise and class noise to achieve an optimal clean set, and (2) support vector machine classifiers, based on the improved particle swarm optimization algorithm, are seen as component classifiers. Then, we obtained the final classification results by combining finally individual prediction through AdaBoosting algorithm on the new sample set. Some experiments are applied on supply chain financial analysis of China’s listed companies. Results indicate that the credit assessment accuracy can be increased by applying this approach.https://doi.org/10.1177/1550147720903631 |
| spellingShingle | Ying Liu Lihua Huang Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination International Journal of Distributed Sensor Networks |
| title | Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination |
| title_full | Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination |
| title_fullStr | Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination |
| title_full_unstemmed | Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination |
| title_short | Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination |
| title_sort | supply chain finance credit risk assessment using support vector machine based ensemble improved with noise elimination |
| url | https://doi.org/10.1177/1550147720903631 |
| work_keys_str_mv | AT yingliu supplychainfinancecreditriskassessmentusingsupportvectormachinebasedensembleimprovedwithnoiseelimination AT lihuahuang supplychainfinancecreditriskassessmentusingsupportvectormachinebasedensembleimprovedwithnoiseelimination |