Resampling approaches to handle class imbalance: a review from a data perspective

This article presents a data-driven review of resampling approaches aimed at mitigating the class imbalance problem in machine learning, a widespread issue that limits classifier performance across numerous sectors. Initially, this research provides an extensive theoretical examination of the class imbalance problem, emphasizing its propensity to amplify existing data difficulty factors, including class overlap, small disjuncts, and noise, thus biasing the model towards the majority class. Acknowledging the significance of detecting and quantifying the synergistic effects between class imbalance and these data difficulty factors, this study surveys metrics formulated to quantify such phenomena in imbalanced…

• FP
  Fundação para a Ciência e a Tecnologia

  Award: B/UI62/10541/2022