Package: ssc
Type: Package
Title: Semi-Supervised Classification Methods
Version: 2.1-0
Authors@R: c(person("Mabel", "González", role = "aut",
                    email = "mabelc@correo.ugr.es",
                    comment = c(ORCID = "0000-0003-0152-444X")),
             person("Osmani", "Rosado-Falcón", role = "aut",
                    email = "osmanir@uclv.cu",
                    comment = c(ORCID = "0000-0002-2639-3354")),
             person("José Daniel", "Rodríguez", role = "aut",
                    email = "josedaniel@uclv.cu",
                    comment = c(ORCID = "0000-0002-8489-4106")),
             person("Christoph", "Bergmeir", role = c("ths", "cre"),
                    email = "c.bergmeir@decsai.ugr.es",
                    comment = c(ORCID = "0000-0002-3665-9021")),
             person("Isaac", "Triguero", role = "ctb",
                    email = "isaac.triguero@nottingham.ac.uk",
                    comment = c(ORCID = "0000-0002-0150-0651")),
             person("José Manuel", "Benítez", role = "ths",
                    email = "j.m.benitez@decsai.ugr.es",
                    comment = c(ORCID = "0000-0002-2346-0793"))
             )
Description: 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.
Depends: R (>= 3.2.3)
Imports: stats, proxy
Suggests: caret, e1071, C50, kernlab, testthat, timeDate, stringi,
        R.rsp
VignetteBuilder: R.rsp
License: GPL (>= 3)
URL: https://github.com/mabelc/SSC
BugReports: https://github.com/mabelc/SSC/issues
Encoding: UTF-8
RoxygenNote: 6.1.0
NeedsCompilation: no
Repository: https://mabelc.r-universe.dev
Date/Publication: 2019-12-16 20:06:18 UTC
RemoteUrl: https://github.com/mabelc/ssc
RemoteRef: HEAD
RemoteSha: 4565f07e0f197e823bcea8442ed9ea82b0c94712
Packaged: 2026-07-14 05:27:59 UTC; root
Author: Mabel González [aut] (ORCID: <https://orcid.org/0000-0003-0152-444X>),
  Osmani Rosado-Falcón [aut] (ORCID:
    <https://orcid.org/0000-0002-2639-3354>),
  José Daniel Rodríguez [aut] (ORCID:
    <https://orcid.org/0000-0002-8489-4106>),
  Christoph Bergmeir [ths, cre] (ORCID:
    <https://orcid.org/0000-0002-3665-9021>),
  Isaac Triguero [ctb] (ORCID: <https://orcid.org/0000-0002-0150-0651>),
  José Manuel Benítez [ths] (ORCID:
    <https://orcid.org/0000-0002-2346-0793>)
Maintainer: Christoph Bergmeir <c.bergmeir@decsai.ugr.es>