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

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

Skewed fat-tailed distributed (imbalance or class-imbalance) datasets pose over- whelming aberrations in numerous machine learning (ML) algorithms, particularly in real-life applications, especially in the domain of credit risk modelling, where default cases (minority-classes) are often outnumbered...

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Bibliographic Details
Main Authors: Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu
Format: Article
Language:English
Published: Sciendo 2025-06-01
Series:Foundations of Computing and Decision Sciences
Subjects:
skewed fat-tailed distributed datasets
diverse features
noisy instances
Online Access:https://doi.org/10.2478/fcds-2025-0009
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https://doi.org/10.2478/fcds-2025-0009

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