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A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis

Author

Listed:
  • Yu, Lean
  • Yao, Xiao
  • Zhang, Xiaoming
  • Yin, Hang
  • Liu, Jia

Abstract

In this paper, a novel dual-weighted fuzzy proximal support vector machine (FPSVM) model hybridizing fuzzy set theory (FST) and proximal support vector machine (PSVM) is proposed for credit risk analysis. In the proposed model, the fuzzy memberships are introduced into both objective function and constraint conditions of PSVM model to make full use of the information of data. Due to the introduction of fuzzy set theory, the FPSVM model shows fine generalized ability and great practical value. For verification purpose, two publicly available credit datasets are used to test the effectiveness of the proposed FPSVM method. Experimental results show that the proposed FPSVM outperforms other SVM models listed in this study, indicating that the proposed FPSVM model has rather good discriminatory power and it can be used as a promising tool for other classification tasks.

Suggested Citation

  • Yu, Lean & Yao, Xiao & Zhang, Xiaoming & Yin, Hang & Liu, Jia, 2020. "A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis," International Review of Financial Analysis, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:finana:v:71:y:2020:i:c:s1057521920302210
    DOI: 10.1016/j.irfa.2020.101577
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    References listed on IDEAS

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    Cited by:

    1. Engin Tas & Ayca Hatice Atli, 2024. "Stock Price Ranking by Learning Pairwise Preferences," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 513-528, February.
    2. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    3. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    4. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    5. Baker, H. Kent & Kumar, Satish & Goyal, Kirti & Sharma, Anuj, 2021. "International review of financial analysis: A retrospective evaluation between 1992 and 2020," International Review of Financial Analysis, Elsevier, vol. 78(C).

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