ROBOSRMSMOTE
Robust Oversampling with RM-SMOTE for Imbalanced Classification
Provides the ROBOSRMSMOTE (Robust Oversampling with RM-SMOTE) framework for imbalanced
classification tasks. This package extends Mahalanobis distance-based
oversampling techniques by integrating robust covariance estimators to
better handle outliers and complex data distributions. The implemented
methodology builds upon and significantly expands the RM-SMOTE algorithm
originally proposed by Taban et al. (2025)
ROBOSRMSMOTE
Overview
ROBOSRMSMOTE (Robust Oversampling with RM-SMOTE) provides a framework for imbalanced classification tasks. This package extends Mahalanobis distance-based oversampling techniques by integrating robust covariance estimators to better handle outliers and complex data distributions. The implemented methodology builds upon and significantly expands the RM-SMOTE algorithm originally proposed by Taban et al. (2025).
Seven robust covariance estimators are supported.
Taban, R., Nunes, C. and Oliveira, M.R. (2025). RM-SMOTE: a new robust balancing technique. Statistical Methods & Applications. https://doi.org/10.1007/s10260-025-00819-8
Installation
install.packages("ROBOSRMSMOTE")
Core Functions
| Function | Description |
|---|---|
ROBOS_RM_SMOTE() |
Main function — generates synthetic minority observations |
weighting() |
Computes robust Mahalanobis weights for minority class |
get_robust_cov() |
Fits one of 7 robust covariance estimators |
Supported Covariance Estimators
cov_method |
Estimator |
|---|---|
"mcd" |
Minimum Covariance Determinant (default) |
"mve" |
Minimum Volume Ellipsoid |
"mest" |
M-estimator |
"mmest" |
MM-estimator |
"sde" |
Stahel-Donoho Estimator |
"sest" |
S-estimator |
"ogk" |
Orthogonalized Gnanadesikan-Kettenring |
Quick Start
library(ROBOSRMSMOTE)
# Load the example dataset (haberman: IR ≈ 2.78, n = 306)
data(haberman)
table(haberman$class)
#> negative positive
#> 225 81
# Balance with ROBOS_RM_SMOTE using MCD (default)
balanced <- ROBOS_RM_SMOTE(dt = haberman, target = "positive", eIR = 1)
table(balanced$class)
#> negative positive
#> 225 225
# Use a different robust estimator
balanced_ogk <- ROBOS_RM_SMOTE(dt = haberman,
target = "positive",
eIR = 1,
cov_method = "ogk",
weight_func = 2) # omega_B weighting
table(balanced_ogk$class)
Weighting Functions
weight_func |
Formula | Behaviour |
|---|---|---|
1 |
ω_A: weight = 0 | Hard exclusion of outliers |
2 |
ω_B: weight = 1/MD² | Soft down-weighting |
3 |
ω_C: weight = τ/MD² | Minimal down-weighting |
License
GPL-3 © Emre Dunder, Mehmet Ali Cengiz, Zainab Subhi Mahmood Hawrami, Abdulmohsen Alharthi