ROSE: a Package for Binary Imbalanced Learning

By Nicola Lunardon, Giovanna Menardi, Nicola Torelli. The R Journal, 2014

471 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/42b6c8c45702a32b4e68395a068cf1be72899713

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ahmed E. Hassan
2. Ken-ichi Matsumoto
3. Chakkrit Kla Tantithamthavorn

In Statistics and Probability

1. Ahmed E. Hassan
2. Ken-ichi Matsumoto
3. Chakkrit Kla Tantithamthavorn

Citations in the corpus

• The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “Thus, we select the ROSE technique [44], [45] in our study.”