AdaSampling: Adaptive Sampling for Positive Unlabeled and Label Noise Learning

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: 1.0
Depends: R (≥ 3.4.0)
Imports: caret (≥ 6.0-78) , class (≥ 7.3-14), e1071 (≥ 1.6-8), stats, MASS
Suggests: knitr, rmarkdown
Published: 2018-06-04
Author: Pengyi Yang & Dinuka Perera
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

Downloads:

Reference manual: AdaSampling.pdf
Vignettes: Breast cancer classification with AdaSampling
Package source: AdaSampling_1.0.tar.gz
Windows binaries: r-devel: AdaSampling_1.0.zip, r-release: AdaSampling_1.0.zip, r-oldrel: AdaSampling_1.0.zip
OS X binaries: r-release: AdaSampling_1.0.tgz, r-oldrel: AdaSampling_1.0.tgz

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