Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination

Ying Liu and Lihua Huang

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 1, 1550147720903631

Abstract: 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.

Keywords: Support vector machines; supply chain financing; credit risk; ensemble learning; noisy training dataset; fuzzy clustering (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:1:p:1550147720903631

DOI: 10.1177/1550147720903631

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