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.
|Author||Mabel 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>)|
|Maintainer||Christoph Bergmeir <email@example.com>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.