Imbalance: Oversampling algorithms for imbalanced classification in R

Addressing imbalanced datasets in classification tasks is a relevant topic in research studies. The main reason is that for standard classification algorithms, the success rate when identifying minority class instances may be adversely affected. Among different solutions to cope with this problem, data level techniques have shown a robust behavior. In this paper, the novel imbalance package is introduced. Written in R and C++, and available at CRAN repository, this library includes recent relevant oversampling algorithms to improve the quality of data in imbalanced datasets, prior to performing a learning task. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript. (C) 2018 Elsevier B.V. All rights reserved.