ssc: Semi-Supervised Classification Methods

Provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Version:	2.1-0
Depends:	R (≥ 3.2.3)
Imports:	stats, proxy
Suggests:	caret, e1071, C50, kernlab, testthat, timeDate, stringi, R.rsp
Published:	2019-12-15
DOI:	10.32614/CRAN.package.ssc
Author:	Mabel González [aut], Osmani Rosado-Falcón [aut], José Daniel Rodríguez [aut], Christoph Bergmeir [ths, cre], Isaac Triguero [ctb], José Manuel Benítez [ths]
Maintainer:	Christoph Bergmeir <c.bergmeir at decsai.ugr.es>
BugReports:	https://github.com/mabelc/SSC/issues
License:	GPL (≥ 3)
URL:	https://github.com/mabelc/SSC
NeedsCompilation:	no
Materials:	README
CRAN checks:	ssc results

Documentation:

Reference manual:	ssc.pdf
Vignettes:	ssc: An R Package for Semi-Supervised Classification

Downloads:

Package source:	ssc_2.1-0.tar.gz
Windows binaries:	r-devel: ssc_2.1-0.zip, r-release: ssc_2.1-0.zip, r-oldrel: ssc_2.1-0.zip
macOS binaries:	r-release (arm64): ssc_2.1-0.tgz, r-oldrel (arm64): ssc_2.1-0.tgz, r-release (x86_64): ssc_2.1-0.tgz, r-oldrel (x86_64): ssc_2.1-0.tgz
Old sources:	ssc archive

Reverse dependencies:

Reverse imports:

cytofQC

Linking:

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