IRIC: An R library for binary imbalanced classification

Abstract

Imbalanced classification is a challenging issue in data mining and machine learning, for which a large number of solutions have been proposed. In this paper, we introduce an R library called IRIC, which integrates a wide set of solutions for imbalanced binary classification. IRIC not only provides a new implementation of some state-of-art techniques for imbalanced classification, but also improves the efficiency of model building using parallel techniques. The library and its source code are made freely available. Keywords: Imbalanced classification, R language, Integrated library, Parallel implementation