Preprocessing Algorithms for Imbalanced Datasets
imabalance: A package to treat imbalanced datasets
Compute imbalance ratio of a binary dataset
Majority weighted minority oversampling technique for imbalance datase...
Fitering of oversampled data based on non-cooperative game theory
Wrapper that encapsulates a collection of algorithms to perform a clas...
Probability density function estimation based oversampling
Plots comparison between the original and the new balanced dataset.
Rapidly converging Gibbs algorithm.
Random walk oversampling
Generic methods to train classifiers
Wrapper for rapidly converging Gibbs algorithm.
Class imbalance usually damages the performance of classifiers. Thus, it is important to treat data before applying a classifier algorithm. This package includes recent resampling algorithms in the literature: (Barua et al. 2014) <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>, (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014) <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014) <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to perform oversampling.