imbalanceKit: Resampling Methods and Tools for Imbalanced Classification
A collection of resampling methods for class imbalance in
classification problems. Includes the oversampling methods SMOTE with
boosting (SMOTEWB) by F. Sağlam and M. A. Cengiz (2022)
<doi:10.1016/j.eswa.2022.117023>, Markov chain SMOTE (MCSMOTE) by
F. Sağlam and M. A. Cengiz (2025) <doi:10.1016/j.asoc.2025.113906>,
uncertainty guided oversampling (UGO) by F. Sağlam and M. A. Cengiz (2026)
<doi:10.1016/j.knosys.2026.115886>, and bi-directional SMOTE (BiSMOTE) by
F. Sağlam and U. Yamak (2027) <doi:10.1016/j.eswa.2026.134648>, and the
data relocation approach DatRel by F. Sağlam (2025)
<doi:10.1007/s10994-025-06755-8>, together
with commonly used alternatives such as SMOTE, ADASYN, Borderline-SMOTE,
Safe-Level-SMOTE, geometric SMOTE, random oversampling and undersampling.
The methods share the same interface: they take a feature matrix and a
factor class variable and return the resampled data together with the
synthetic samples.
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