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.

To cite the SMOTEWB method in publications use:

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

To cite the MCSMOTE method in publications use:

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

To cite the UGO method in publications use:

Sağlam, F., & Cengiz, M. A. (2026). Uncertainty guided oversampling: A classifier-specific approach for effective handling of class imbalance in machine learning. Knowledge-Based Systems, 342, 115886. https://doi.org/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. https://doi.org/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. https://doi.org/10.1007/s10994-025-06755-8

To cite the EBSMOTE method in publications use:

Hussein, A. S., 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) (pp. 1226-1233). IEEE.

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},
  }