SMOTEWB - Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)

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 each 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.

Last updated 8 months ago

3.95 score 1 stars 1 packages 12 scripts 221 downloads

rcccd - Class Cover Catch Digraph Classification

Fit Class Cover Catch Digraph Classification models that can be used in machine learning. Pure and proper and random walk approaches are available. Methods are explained in Priebe et al. (2001) <doi:10.1016/S0167-7152(01)00129-8>, Priebe et al. (2003) <doi:10.1007/s00357-003-0003-7>, and Manukyan and Ceyhan (2016) <doi:10.48550/arXiv.1904.04564>.

Last updated 6 months ago

3.78 score 2 stars 1 packages 7 scripts 107 downloads

imbalanceDatRel - Relocated Data Oversampling for Imbalanced Data Classification

Relocates oversampled data from a specific oversampling method to cover area determined by pure and proper class cover catch digraphs (PCCCD). It prevents any data to be generated in class overlapping area.

Last updated 7 months ago

3.00 score 88 downloads