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AdaSampling: Adaptive Sampling for Positive Unlabeled and Label NoiseLearning

Implements the adaptive sampling procedure, a framework for both positive unlabeled learning and learning with class label noise. Yang, P., Ormerod, J., Liu, W., Ma, C., Zomaya, A., Yang, J. (2018) <doi:10.1109/TCYB.2018.2816984>.

Version:1.3
Depends:R (≥ 3.4.0)
Imports:caret (≥ 6.0-78) ,class (≥ 7.3-14),e1071 (≥ 1.6-8), stats,MASS
Suggests:knitr,rmarkdown
Published:2019-05-21
DOI:10.32614/CRAN.package.AdaSampling
Author:Pengyi Yang
Maintainer:Pengyi Yang <yangpy7 at gmail.com>
BugReports:https://github.com/PengyiYang/AdaSampling/issues
License:GPL-3
URL:https://github.com/PengyiYang/AdaSampling/
NeedsCompilation:no
Materials:README
CRAN checks:AdaSampling results

Documentation:

Reference manual:AdaSampling.html ,AdaSampling.pdf
Vignettes:Breast cancer classification with AdaSampling (source,R code)

Downloads:

Package source: AdaSampling_1.3.tar.gz
Windows binaries: r-devel:AdaSampling_1.3.zip, r-release:AdaSampling_1.3.zip, r-oldrel:AdaSampling_1.3.zip
macOS binaries: r-release (arm64):AdaSampling_1.3.tgz, r-oldrel (arm64):AdaSampling_1.3.tgz, r-release (x86_64):AdaSampling_1.3.tgz, r-oldrel (x86_64):AdaSampling_1.3.tgz
Old sources: AdaSampling archive

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=AdaSamplingto link to this page.


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