SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering

583 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Class Boundary Exemplar Selection Based Incremental Learning for Automatic Target Recognition (2020, background): “This is a basic operation to remove the “outliers” in other classes’ distributions, which is similar to [27].”
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “Another approach is to apply a synergy of preprocessing models, i.e. filtering and/or instance generation to remove those instances that are actually noisy prior to the SMOTE application (Sáez et al., 2015; Verbiest, Ramentol, Cornelis, & Herrera, 2014).”