ROSE: a Package for Binary Imbalanced Learning

Authors

Nicola Lunardon, University of Trieste
Giovanna Menardi, University of Padua
Nicola Torelli, University of Trieste

Date of this Version

6-2014

Document Type

Article

Citation

The R Journal (June 2014) 6(1); Editor: Deepayan Sarkar

Comments

Copyright 2014, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

The ROSE package provides functions to deal with binary classification problems in the presence of imbalanced classes. Artificial balanced samples are generated according to a smoothed bootstrap approach and allow for aiding both the phases of estimation and accuracy evaluation of a binary classifier in the presence of a rare class. Functions that implement more traditional remedies for the class imbalance and different metrics to evaluate accuracy are also provided. These are estimated by holdout, bootstrap, or cross-validation methods.