Description
Imbalanced Resampling using SMOTE with Boosting (SMOTEWB).
Description
Provides the SMOTE with Boosting (SMOTEWB) algorithm. See F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a SMOTE-based resampling technique which creates synthetic data on the links between nearest neighbors. SMOTEWB uses boosting weights to determine where to generate new samples and automatically decides the number of neighbors for eacg sample. It is robust to noise and outperforms most of the alternatives according to Matthew Correlation Coefficient metric. Alternative resampling methods are also available in the package.
README.md
SMOTEWB
Sağlam and Mehmet's (2022) SMOTE with Boosting (SMOTEWB) oversampling algorithm for imbalanced datasets.
The package also includes faster versions of popular resampling methods, ADASYN, Borderline SMOTE (BLSMOTE), Random Over-Sampling (ROS), Random Under-Sampling (RUS), Safe-Level SMOTE (SLSMOTE), Relocating Safe-Level SMOTE (RSLSMOTE), and Random Over-Sampling Examples (ROSE).
R installation
devtools::install_github("https://github.com/fatihsaglam/SMOTEWB")
References
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.