Robust Oversampling with RM-SMOTE for Imbalanced Classification.

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

Supported Covariance Estimators

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

License

GPL-3 © Emre Dunder, Mehmet Ali Cengiz, Zainab Subhi Mahmood Hawrami, Abdulmohsen Alharthi.