A heuristic bilevel optimization wrapper around SVM-SMOTE is claimed to improve minority-class F1 by selecting training samples that increase model-output variance and reduce overlap.
Imbalanced multi-label data classification as a bi-level optimization problem: application to miRNA-related diseases diagnosis
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Sampling Imbalanced Data with Multi-objective Bilevel Optimization
A heuristic bilevel optimization wrapper around SVM-SMOTE is claimed to improve minority-class F1 by selecting training samples that increase model-output variance and reduce overlap.