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A Novel Fuzzy Unsupervised Quadratic Surface Support Vector Machine Based on DC Programming: An Application to Credit Risk Management

Author

Listed:
  • Tao Yu

    (School of Mathematics, Harbin Institute of Technology, Harbin 150001, China
    Department of Mathematics, Southern University of Science and Technology, Shenzhen 518055, China)

  • Wei Huang

    (College of Business, Southern University of Science and Technology, Shenzhen 518055, China
    National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, China)

  • Xin Tang

    (College of Business, Southern University of Science and Technology, Shenzhen 518055, China
    National Center for Applied Mathematics Shenzhen, Southern University of Science and Technology, Shenzhen 518055, China)

Abstract

Unsupervised classification is used in credit risk assessment to reduce human resource costs and make informed decisions in the shortest possible time. Although several studies show that support vector machine-based methods have better performance in unlabeled datasets, several factors still negatively affect these models, such as unstable results due to random initialization, reduced effectiveness due to kernel dependencies, and noise points and outliers. This paper introduces an unsupervised classification method based on a fuzzy unsupervised quadratic surface support vector machine without a kernel to avoid selecting related kernel parameters for credit risk assessment. In addition, we propose an innovative fuzzy membership function for reducing noise points and outliers in line with the direction of sample density variation. Fuzzy Unsupervised QSSVM (FUS-QSSVM) outperforms well-known SVM-based methods based on numerical tests on public benchmark credit data. In some real-world applications, the proposed method has significant potential as well as being effective, efficient, and robust. The algorithm can therefore increase the number of potential customers of financial institutions as well as increase profitability.

Suggested Citation

  • Tao Yu & Wei Huang & Xin Tang, 2023. "A Novel Fuzzy Unsupervised Quadratic Surface Support Vector Machine Based on DC Programming: An Application to Credit Risk Management," Mathematics, MDPI, vol. 11(22), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4661-:d:1281445
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    References listed on IDEAS

    as
    1. Luo, Jian & Yan, Xin & Tian, Ye, 2020. "Unsupervised quadratic surface support vector machine with application to credit risk assessment," European Journal of Operational Research, Elsevier, vol. 280(3), pages 1008-1017.
    2. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    3. Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Hagar Ahmed, 2023. "Breast Cancer Diagnosis Using a Novel Parallel Support Vector Machine with Harris Hawks Optimization," Mathematics, MDPI, vol. 11(14), pages 1-25, July.
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