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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.