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Cited by 214Explainable Artificial Intelligence (XAI)Imbalanced Data Classification TechniquesFinancial Distress and Bankruptcy Prediction

The DOI matches its Crossref record; 68 of the 71 references it cites were found in a registry or an index.

Interpretable machine learning for imbalanced credit scoring datasets

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Chen, Yujia, Calabrese, Raffaella, Martin-Barragan, Belen·2024·European Journal of Operational Research
DOI 10.1016/j.ejor.2023.06.036
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Abstract

The class imbalance problem is common in the credit scoring domain, as the number of defaulters is usually much less than the number of non-defaulters. To date, research on investigating the class imbalance problem has mainly focused on indicating and reducing the adverse effect of the class imbalance on the predictive accuracy of machine learning techniques, while the impact of that on machine learning interpretability has never been studied in the literature. This paper fills this gap by analysing how the stability of Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), two popular interpretation methods, are affected by class imbalance. Our experiments use 2016-2020 UK residential mortgage data collected from European Datawarehouse. We evaluate the stability of LIME and SHAP on datasets of progressively increased class imbalance. The results show that interpretations generated from LIME and SHAP are less stable as the class imbalance increases, which indicates that the class imbalance does have an adverse effect on machine learning interpretability. To check the robustness of our outcomes, we also analyse two open-source credit scoring datasets and we obtain similar results.

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Research works printed but not linked: 3

  1. Ekstrøm, 2019, Sequential rank agreement methods for comparison of ranked lists, Biostatistics, 20, 582, 10.1093/biostatistics/kxy017 · DOI
  2. Thomas, 2017, vol. 6
  3. Wachter, 2017, Counterfactual explanations without opening the black box: Automated decisions and the GDPR, SSRN Electronic Journal, 10.2139/ssrn.3063289 · DOI · Paper page

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Metadata

Authors
Chen, Y., Calabrese, R., & Martin-Barragan, B.
Affiliations
University of Edinburgh (GB)
Corresponding author
Chen, Yujia
ORCID
Venue
European Journal of Operational Research
Publisher
Elsevier BV
Published
2024-01
Volume
312
Issue
1
Pages
357-372
ISSN
0377-2217 (ISSN-L)
DOI
10.1016/j.ejor.2023.06.036
OpenAlex
W4381997116
Cited by
190
References
71
Citations by year
2026: 72 · 2025: 62 · 2024: 49
FWCI
16.55
Type
journal article
Language
en
Open access
Licence
Locations
  • European Journal of Operational Research (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
Funders
Economic and Social Research Council (ES/W010259/1)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
Keywords
class imbalance, credit scoring, interpretability, lime, SHAP, explanation stability, mortgage default, residential mortgages, default prediction

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