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How hard is my imbalanced classification task? - An explainable supervised approach
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DOI：10.1016/j.eswa.2025.130695.png)
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Abstract
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• First end-to-end explainable framework predicting dataset difficulty and causes. • GPU-optimized complexity meta-features for overlap, noise, nonlinearity. • Novel methods are proposed for scalable complexity metrics in multiclass settings. • EBMs & GAMformer act as meta-models providing accuracy and interpretability. • Tested on 613 binary & 91 multiclass imbalanced datasets.
Keywords:
Imbalanced learning
Complexity metrics
In-context learning
Data difficulty factors
Explainability
Explainable boosting machines
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