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

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  • Ying Liu
  • Lihua Huang

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.

Suggested Citation

  • Ying Liu & Lihua Huang, 2020. "Supply chain finance credit risk assessment using support vector machine–based ensemble improved with noise elimination," International Journal of Distributed Sensor Networks, , vol. 16(1), pages 15501477209, January.
  • Handle: RePEc:sae:intdis:v:16:y:2020:i:1:p:1550147720903631
    DOI: 10.1177/1550147720903631
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    References listed on IDEAS

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    1. René Caldentey & Martin B. Haugh, 2009. "Supply Contracts with Financial Hedging," Operations Research, INFORMS, vol. 57(1), pages 47-65, February.
    2. Jie Cao & Hongke Lu & Weiwei Wang & Jian Wang, 2012. "A Novel Five-Category Loan-Risk Evaluation Model Using Multiclass Ls-Svm By Pso," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(04), pages 857-874.
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    Cited by:

    1. Ma, Rui & Mao, Di & Cao, Dongmei & Luo, Shuai & Gupta, Suraksha & Wang, Yichuan, 2024. "From vineyard to table: Uncovering wine quality for sales management through machine learning," Journal of Business Research, Elsevier, vol. 176(C).
    2. Haibo Wang & Lutfu S. Sua & Bahram Alidaee, 2024. "Enhancing supply chain security with automated machine learning," Papers 2406.13166, arXiv.org.
    3. Meiyan Li & Yingjun Fu, 2022. "Prediction of Supply Chain Financial Credit Risk Based on PCA-GA-SVM Model," Sustainability, MDPI, vol. 14(24), pages 1-21, December.

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