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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk Cover

Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk

Foundations of Computing and Decision Sciences
Volume 50 (2025): Issue 2 (June 2025)
By: Keith R Musara,  Edmore Ranganai,  Charles Chimedza,  Florence Matarise and  Sheunesu Munyira  
Open Access
|Jun 2025

Authors

Keith R Musara

musarakeith@gmail.com

Faculty of Science, University of Zimbabwe, Zimbabwe

Edmore Ranganai

rangae@unisa.ac.za

Faculty of Science, University of South Africa, South Africa

Charles Chimedza

charles.chimedza@wits.ac.za

School of Statistics, University of the Witwatersrand, South Africa

Florence Matarise

matarise7@gmail.com

Faculty of Science, University of Zimbabwe, Zimbabwe

Sheunesu Munyira

munyirask@gmail.com

Faculty of Science, University of Zimbabwe, Zimbabwe
DOI: https://doi.org/10.2478/fcds-2025-0009 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356
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Language: English
Page range: 229 - 270
Submitted on: Aug 31, 2024
Accepted on: Feb 13, 2025
Published on: Jun 10, 2025
Published by: Poznan University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Skewed fat-tailed distributed datasets,
Diverse features,
Noisy instances
Related subjects:
Computer sciences,
Artificial intelligence,
Software development

© 2025 Keith R Musara, Edmore Ranganai, Charles Chimedza, Florence Matarise, Sheunesu Munyira, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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