A Novel Multiclass Imbalance Classification Framework With Dynamic Evidential Fusion for Credit Rating

Wen‐hui Hou, Xiao‐kang Wang, Min‐hui Deng, Hong‐yu Zhang and Jian‐qiang Wang

Journal of Forecasting, 2026, vol. 45, issue 1, 335-352

Abstract: Credit rating serves as a crucial instrument for lenders to evaluate borrowers' creditworthiness and mitigate the risk of nonperforming loans. However, credit rating tasks often face significant challenges due to multiclass distributions and severe class imbalances. Given the advantages of ensemble learning methods in addressing these challenges, this study presents a novel multiclass imbalance classification framework that integrates the Error Correcting Output Codes (ECOC) decomposition approach with diverse dichotomizer imbalance algorithms to enhance credit ratings. Nevertheless, selecting and quantifying the uncertainty of dichotomizer sets poses challenges. To this end, we introduce a dynamic ensemble selection strategy and evidence theory within the ECOC setup. By tailoring specific dichotomizers to individual samples and consolidating uncertain binary outcomes using belief functions, a resilient ensemble classifier is developed. Extensive experiments on nine KEEL benchmark datasets and two real credit datasets demonstrate its effectiveness in handling severe imbalance in credit rating tasks.

Date: 2026
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https://doi.org/10.1002/for.70042

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Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:45:y:2026:i:1:p:335-352

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