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Introduces K-IPO, a generate-then-select oversampling framework that preserves the original data's feature importance ranking (measured by Kendall's tau) during augmentation for imbalanced tabular data, showing improved preservation, explanation consistency, and predictive performance across 20 datasets.
Proposes a Hierarchy-Aware RoBERTa framework for classifying cybersecurity vulnerabilities in the CWE taxonomy, demonstrating that hierarchy-aware representation learning is more effective than oversampling techniques for handling class imbalance.
RUBRIC is a generator-agnostic filtering framework for imbalanced classification that selects synthetic samples by balancing realism (via a discriminator) and utility (margin-based scoring), improving F1-macro and recall on benchmarks like credit-card fraud detection.