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.


Reference manual

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2.1-0 by Christoph Bergmeir, a year ago

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Authors: Mabel González [aut] , Osmani Rosado-Falcón [aut] , José Daniel Rodríguez [aut] , Christoph Bergmeir [ths, cre] , Isaac Triguero [ctb] , José Manuel Benítez [ths]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports stats, proxy

Suggests caret, e1071, C50, kernlab, testthat, timeDate, stringi, R.rsp

See at CRAN