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

Lean Yu, Xiao Yao, Xiaoming Zhang, Hang Yin, Jia Liu

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Article number101577
Number of pages10
JournalInternational Review of Financial Analysis
Volume71
Early online date8 Sep 2020
DOIs
Publication statusPublished - 1 Oct 2020

Keywords

  • Credit risk analysis
  • Support vector machine
  • Fuzzy memberships
  • Dual-weighted fuzzy PSVM

Fingerprint

Dive into the research topics of 'A novel dual-weighted fuzzy proximal support vector machine with application to credit risk analysis'. Together they form a unique fingerprint.

Cite this