Hybrid approaches address such trade-offs by combining oversampling with post-resampling cleaning strategies that remove noisy or duplicate samples. A classic example is SMOTE combined with Edited Nearest Neighbors (SMOTE+ENN), which, after oversampling minority-class samples through synthetic generation techniques, performs neighbourhood cleaning to remove uncertain samples, thereby increasing both class separability and instability \cite{Fernandez18}. Hybrid approaches are typical for biomedical machine learning, as high-throughput data is often noisy, and the value of a feature often depends on other features

Hybrid methods tackle these trade-offs by merging oversampling techniques with cleaning procedures that eliminate noisy or redundant samples after resampling. SMOTE paired with Edited Nearest Neighbors (SMOTE+ENN) serves as a well-known illustration, where synthetic minority samples are first generated through oversampling, followed by neighborhood-based cleaning that removes ambiguous samples to enhance class separability and reduce instability \cite{Fernandez18}. These hybrid strategies are commonly used in biomedical machine learning applications, since high-throughput datasets frequently contain noise and feature values typically interact with one another in complex ways.