Package: imbalanceKit 0.1.0

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.

Authors:Fatih Saglam [aut, cre]