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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 |
Reference manual: | imbalance.pdf |
Vignettes: |
Working with imbalanced dataset |
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 suggests: | randomForestSRC, rbooster |
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.