A Comparative Study of Machine Learning Models for Micro-Segment Credit Risk Prediction

Date

2026-03-19

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers Inc.

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

relationships.isProjectOf

relationships.isJournalIssueOf

Abstract

Description

Keywords

Feature Engineering, Imbalanced Learning, Gradient Boosting, Micro-Segment Credit Risk, Inflation Adjustment, Macro F1-Score

Fields of Science

Citation

WoS Q

Scopus Q

OpenCitations Logo
OpenCitations Citation Count
N/A

Source

Proceedings of the International Symposium on Digital Forensics and Security, ISDFS -- 14th International Symposium on Digital Forensics and Security, ISDFS 2026 -- 19 March 2026 through 20 March 2026 -- Boston -- 222184

Volume

Issue

2026

Start Page

End Page

URI

https://hdl.handle.net/20.500.11779/3814
 https://doi.org/10.1109/ISDFS69419.2026.11459062

Collections

Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
PlumX Metrics
Citations

Scopus : 0

Google Scholar Logo
Google Scholar™
Check
OpenAlex Logo
OpenAlex FWCI
0.00

Sustainable Development Goals

SDG data could not be loaded because of an error. Please refresh the page or try again later.