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Knowledge Coverage-Guided Undersampling Ensemble for imbalanced classification
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DOI：10.1016/j.patcog.2026.115232.png)
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Abstract
En 中文
• KCUE formulates undersampling as majority-class knowledge coverage maximization.
• An inter-class separation index captures local majority–minority separability.
• A Poisson coverage function quantifies cumulative majority-class information.
• Submodularity guides marginal-gain sampling to reduce redundant acquisition.
Keywords:
Undersampling
Ensemble
Knowledge coverage
Imbalanced learning
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7.6
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1.4W
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5.4W
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