Authors and Citation
Citation
Sağlam, F. (2026). imbalanceKit: Resampling Methods and Tools for Imbalanced Classification. R package version 0.2.0.
@Manual{,
title = {imbalanceKit: Resampling Methods and Tools for Imbalanced Classification},
author = {Fatih Sağlam},
year = {2026},
note = {R package version 0.2.0},
url = {https://github.com/fatihsaglam/imbalanceKit},
}
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
@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},
}
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
@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},
}
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
@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},
}
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
@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},
}
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
@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},
}
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.
@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},
}
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.
@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},
}
Tao, L. (2025). Local entropy-adversarial oversampling for imbalanced datasets. Neurocomputing, 131959.
@Article{,
title = {Local entropy-adversarial oversampling for imbalanced datasets},
author = {L. Tao},
journal = {Neurocomputing},
year = {2025},
pages = {131959},
}
Kunakorntum, I., Hinthong, W., & Phunchongharn, P. (2020). A synthetic minority based on probabilistic distribution (SyMProD) oversampling for imbalanced datasets. IEEE Access, 8, 114692-114704.
@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},
}
Pradipta, G. A., Wardoyo, R., Musdholifah, A., & Sanjaya, I. N. H. (2021). Radius-SMOTE: a new oversampling technique of minority samples based on radius distance for learning from imbalanced data. IEEE Access, 9, 74763-74777.
@Article{,
title = {Radius-SMOTE: a new oversampling technique of minority samples based on radius distance for learning from imbalanced data},
author = {G. A. Pradipta and R. Wardoyo and A. Musdholifah and I. N. H. Sanjaya},
journal = {IEEE Access},
year = {2021},
volume = {9},
pages = {74763--74777},
}
Qiu, F., Yang, F., Xiong, Y., Chen, T., Xiao, P., Zheng, W., ... & Zhou, K. (2025). VS-SMOTE: Leveraging high-value spaces to balance noise control and data diversity for class imbalanced learning. Neurocomputing, 654, 131321.
@Article{,
title = {VS-SMOTE: Leveraging high-value spaces to balance noise control and data diversity for class imbalanced learning},
author = {F. Qiu and F. Yang and Y. Xiong and T. Chen and P. Xiao and W. Zheng and K. Zhou},
journal = {Neurocomputing},
year = {2025},
volume = {654},
pages = {131321},
}