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: 2.0.0
Depends: R (≥ 3.2.3)
Imports: stats, proxy
Suggests: caret, e1071, C50, kernlab, testthat
Published: 2018-03-27
Author: Mabel González ORCID iD [aut], Osmani Rosado-Falcón ORCID iD [aut], José Daniel Rodríguez ORCID iD [aut], Christoph Bergmeir ORCID iD [ths, cre], Isaac Triguero ORCID iD [ctb], José Manuel Benítez ORCID iD [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

Downloads:

Reference manual: ssc.pdf
Package source: ssc_2.0.0.tar.gz
Windows binaries: r-devel: ssc_2.0.0.zip, r-release: ssc_2.0.0.zip, r-oldrel: ssc_2.0.0.zip
OS X binaries: r-release: ssc_2.0.0.tgz, r-oldrel: not available
Old sources: ssc archive

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