ssc: Semi-Supervised Classification Methods
Version 2.0.0

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.

Getting started

Package details

AuthorMabel González [aut] (<https://orcid.org/0000-0003-0152-444X>), Osmani Rosado-Falcón [aut] (<https://orcid.org/0000-0002-2639-3354>), José Daniel Rodríguez [aut] (<https://orcid.org/0000-0002-8489-4106>), Christoph Bergmeir [ths, cre] (<https://orcid.org/0000-0002-3665-9021>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), José Manuel Benítez [ths] (<https://orcid.org/0000-0002-2346-0793>)
Date of publication2018-03-27 04:00:31 UTC
MaintainerChristoph Bergmeir <[email protected]>
LicenseGPL (>= 3)
Version2.0.0
URL https://github.com/mabelc/SSC
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("ssc")

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ssc documentation built on April 1, 2018, 12:23 p.m.