To cite the imbalanceKit package in publications use:

Sağlam F (2026). imbalanceKit: Resampling Methods and Tools for Imbalanced Classification. R package version 0.1.0, https://github.com/fatihsaglam/imbalanceKit.

To cite the SMOTEWB method in publications use:

Sağlam F, Cengiz M (2022). “A novel SMOTE-based resampling technique trough noise detection and the boosting procedure.” Expert Systems with Applications, 200, 117023. doi:10.1016/j.eswa.2022.117023.

To cite the MCSMOTE method in publications use:

Sağlam F, Cengiz M (2025). “MCSMOTE: A transition matrix-driven oversampling technique for class imbalance.” Applied Soft Computing, 185, 113906. doi:10.1016/j.asoc.2025.113906.

To cite the UGO method in publications use:

Sağlam F, Cengiz M (2026). “Uncertainty guided oversampling: A classifier-specific approach for effective handling of class imbalance in machine learning.” Knowledge-Based Systems, 342, 115886. doi:10.1016/j.knosys.2026.115886.

To cite the BiSMOTE method in publications use:

Sağlam F, Yamak U (2027). “Bi-SMOTE: bi-directional oversampling for reducing class overlap and synthetic noise.” Expert Systems with Applications, 334, 134648. doi:10.1016/j.eswa.2026.134648.

To cite the DatRel method in publications use:

Sağlam F (2025). “DatRel: a noise-tolerant data relocation approach for effective synthetic data generation in imbalanced classifiers.” Machine Learning. doi:10.1007/s10994-025-06755-8.

To cite the EBSMOTE method in publications use:

Hussein A, Diallo B, Liu J (2019). “EBSMOTE: Evaluation-based synthetic minority oversampling technique for imbalanced dataset learning.” In 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 1226–1233.

To cite the GQEO method in publications use:

Dai Q, Wang L, Zhang J, Ding W, Chen L (2025). “GQEO: Nearest neighbor graph-based generalized quadrilateral element oversampling for class-imbalance problem.” Neural Networks, 184, 107107.

To cite the LEAO method in publications use:

Tao L (2025). “Local entropy-adversarial oversampling for imbalanced datasets.” Neurocomputing, 131959.

To cite the SyMProD method in publications use:

Kunakorntum I, Hinthong W, Phunchongharn P (2020). “A synthetic minority based on probabilistic distribution (SyMProD) oversampling for imbalanced datasets.” IEEE Access, 8, 114692–114704.

Corresponding BibTeX entries:

  @Manual{,
    title = {imbalanceKit: Resampling Methods and Tools for Imbalanced
      Classification},
    author = {Fatih Sağlam},
    year = {2026},
    note = {R package version 0.1.0},
    url = {https://github.com/fatihsaglam/imbalanceKit},
  }

  @Article{,
    title = {A novel SMOTE-based resampling technique trough noise
      detection and the boosting procedure},
    author = {Fatih Sağlam and Mehmet Ali Cengiz},
    journal = {Expert Systems with Applications},
    year = {2022},
    volume = {200},
    pages = {117023},
    doi = {10.1016/j.eswa.2022.117023},
  }

  @Article{,
    title = {MCSMOTE: A transition matrix-driven oversampling technique
      for class imbalance},
    author = {Fatih Sağlam and Mehmet Ali Cengiz},
    journal = {Applied Soft Computing},
    year = {2025},
    volume = {185},
    pages = {113906},
    doi = {10.1016/j.asoc.2025.113906},
  }

  @Article{,
    title = {Uncertainty guided oversampling: A classifier-specific
      approach for effective handling of class imbalance in machine
      learning},
    author = {Fatih Sağlam and Mehmet Ali Cengiz},
    journal = {Knowledge-Based Systems},
    year = {2026},
    volume = {342},
    pages = {115886},
    doi = {10.1016/j.knosys.2026.115886},
  }

  @Article{,
    title = {Bi-SMOTE: bi-directional oversampling for reducing class
      overlap and synthetic noise},
    author = {Fatih Sağlam and Umut Yamak},
    journal = {Expert Systems with Applications},
    year = {2027},
    volume = {334},
    pages = {134648},
    doi = {10.1016/j.eswa.2026.134648},
  }

  @Article{,
    title = {DatRel: a noise-tolerant data relocation approach for
      effective synthetic data generation in imbalanced classifiers},
    author = {Fatih Sağlam},
    journal = {Machine Learning},
    year = {2025},
    doi = {10.1007/s10994-025-06755-8},
  }

  @InProceedings{,
    title = {EBSMOTE: Evaluation-based synthetic minority oversampling
      technique for imbalanced dataset learning},
    author = {A. S. Hussein and B. Diallo and J. Liu},
    booktitle = {2019 IEEE 14th International Conference on Intelligent
      Systems and Knowledge Engineering (ISKE)},
    year = {2019},
    pages = {1226--1233},
    publisher = {IEEE},
  }

  @Article{,
    title = {GQEO: Nearest neighbor graph-based generalized
      quadrilateral element oversampling for class-imbalance problem},
    author = {Q. Dai and L. Wang and J. Zhang and W. Ding and L. Chen},
    journal = {Neural Networks},
    year = {2025},
    volume = {184},
    pages = {107107},
  }

  @Article{,
    title = {Local entropy-adversarial oversampling for imbalanced
      datasets},
    author = {L. Tao},
    journal = {Neurocomputing},
    year = {2025},
    pages = {131959},
  }

  @Article{,
    title = {A synthetic minority based on probabilistic distribution
      (SyMProD) oversampling for imbalanced datasets},
    author = {I. Kunakorntum and W. Hinthong and P. Phunchongharn},
    journal = {IEEE Access},
    year = {2020},
    volume = {8},
    pages = {114692--114704},
  }