imbalance: Preprocessing Algorithms for Imbalanced Datasets

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.

Depends: R (≥ 3.3.0)
Imports: bnlearn, KernelKnn, ggplot2, utils, stats, mvtnorm, Rcpp, smotefamily, FNN, C50
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown
Published: 2020-04-07
DOI: 10.32614/CRAN.package.imbalance
Author: Ignacio Cordón [aut, cre], Salvador García [aut], Alberto Fernández [aut], Francisco Herrera [aut]
Maintainer: Ignacio Cordón <nacho.cordon.castillo at>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: yes
Citation: imbalance citation info
Materials: README NEWS
CRAN checks: imbalance results


Reference manual: imbalance.pdf
Vignettes: Working with imbalanced dataset


Package source: imbalance_1.0.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): imbalance_1.0.2.1.tgz, r-oldrel (arm64): imbalance_1.0.2.1.tgz, r-release (x86_64): imbalance_1.0.2.1.tgz, r-oldrel (x86_64): imbalance_1.0.2.1.tgz
Old sources: imbalance archive

Reverse dependencies:

Reverse suggests: randomForestSRC, rbooster


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