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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.

Version: 1.0.2.1
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 gmail.com>
BugReports: http://github.com/ncordon/imbalance/issues
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
URL: http://github.com/ncordon/imbalance
NeedsCompilation: yes
Citation: imbalance citation info
Materials: README NEWS
CRAN checks: imbalance results

Documentation:

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

Downloads:

Package source: imbalance_1.0.2.1.tar.gz
Windows binaries: r-devel: imbalance_1.0.2.1.zip, r-release: imbalance_1.0.2.1.zip, r-oldrel: imbalance_1.0.2.1.zip
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

Linking:

Please use the canonical form https://CRAN.R-project.org/package=imbalance to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.