Crossmark

Adaptive fuzzy semi-supervised support vector machine based on sample tightness and its application in credit risk assessment

Published Online: 2026-01-27

Published Print: 2026-01

Authors

Quan, Jing https://orcid.org/0000-0002-7999-2190
Sun, Xuelian
Funding

Funding for this research was provided by:

the Natural Science Foundation of Chongqing (No.cstc 2021jcyj-msxmX0388)
License Information

Text and Data Mining valid from 2026-01-01

Version of Record valid from 2026-01-01
More Information

Article History

Received: 9 May 2025

Accepted: 25 October 2025

First Online: 27 January 2026

Declarations

:

: The authors declare that there are no conflict of interest regarding the publication of this article.

: This article does not contain any studies with human participants performed by any of the authors.

: No informed consent was needed as this study did not include human subjects.

Document is current