Package: SMOTEWB 1.2.5

SMOTEWB: Imbalanced Resampling using SMOTE with Boosting (SMOTEWB)

Provides the SMOTE with Boosting (SMOTEWB) algorithm. See F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a SMOTE-based resampling technique which creates synthetic data on the links between nearest neighbors. SMOTEWB uses boosting weights to determine where to generate new samples and automatically decides the number of neighbors for each sample. It is robust to noise and outperforms most of the alternatives according to Matthew Correlation Coefficient metric. Alternative resampling methods are also available in the package.

Authors:Fatih Saglam [aut, cre]

SMOTEWB_1.2.5.tar.gz
SMOTEWB_1.2.5.zip(r-4.7)SMOTEWB_1.2.5.zip(r-4.6)SMOTEWB_1.2.5.zip(r-4.5)
SMOTEWB_1.2.5.tgz(r-4.6-any)SMOTEWB_1.2.5.tgz(r-4.5-any)
SMOTEWB_1.2.5.tar.gz(r-4.7-any)SMOTEWB_1.2.5.tar.gz(r-4.6-any)
SMOTEWB_1.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
SMOTEWB/json (API)

# Install 'SMOTEWB' in R:
install.packages('SMOTEWB', repos = c('https://fatihsaglam.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/fatihsaglam/smotewb/issues

On CRAN:

Conda:

4.10 score 1 stars 1 packages 14 scripts 268 downloads 11 exports 8 dependencies

Last updated from:164af2621c. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK113
source / vignettesOK264
linux-release-x86_64OK119
macos-release-arm64OK143
macos-oldrel-arm64OK181
windows-develOK77
windows-releaseOK73
windows-oldrelOK84
wasm-releaseOK98

Exports:ADASYNBLSMOTEGSMOTEROSROSERSLSMOTERUSRWOSLSMOTESMOTESMOTEWB

Dependencies:FNNRANNRcppRcppArmadilloRcppParallelRfastrpartzigg