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 distance-based 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 suitable base classifier and distance measure. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Author
Mabel González [aut], José Daniel Rodríguez [aut], Osmani Rosado [aut], José Manuel Benítez [ths], Christoph Bergmeir [ths, cre], Isaac Triguero [ctb]
Date of publication
2016-10-05 09:30:01
Maintainer
Christoph Bergmeir <c.bergmeir@decsai.ugr.es>
License
GPL-2
Version
1.0

View on CRAN

Man pages

bClassif
Base Classifier Specification
bClassifOneNN
1-NN classifier specification builder
coBC
Train the Co-bagging model
coffee
Time series data set
democratic
Train the Democratic model
oneNN
1-NN supervised classifier builder
predict.coBC
Model Predictions
predict.democratic
Model Predictions
predict.OneNN
Model Predictions
predict.selfTraining
Model Predictions
predict.setred
Model Predictions
predict.snnrce
Model Predictions
predict.triTraining
Model Predictions
selfTraining
Train the Self-training model
setred
Train the SETRED model
snnrce
Train the SNNRCE model
statistics
Statistics calculation
triTraining
Train the Tri-training model
wine
Wine recognition data

Files in this package

ssc
ssc/NAMESPACE
ssc/demo
ssc/demo/CoBC.R
ssc/demo/SNNRCE.R
ssc/demo/Democratic.R
ssc/demo/SelfTraining.R
ssc/demo/SETRED.R
ssc/demo/TriTraining.R
ssc/demo/00Index
ssc/demo/CoffeeEx.R
ssc/data
ssc/data/wine.RData
ssc/data/coffee.RData
ssc/R
ssc/R/CoBC.R
ssc/R/Utils.R
ssc/R/SNNRCE.R
ssc/R/BaseClassifierOneNN.R
ssc/R/Democratic.R
ssc/R/SelfTraining.R
ssc/R/BaseClassifier.R
ssc/R/SemiSupervised.R
ssc/R/SETRED.R
ssc/R/TriTraining.R
ssc/R/Statistics.R
ssc/R/DataSets.R
ssc/MD5
ssc/DESCRIPTION
ssc/man
ssc/man/democratic.Rd
ssc/man/wine.Rd
ssc/man/coBC.Rd
ssc/man/predict.setred.Rd
ssc/man/triTraining.Rd
ssc/man/predict.selfTraining.Rd
ssc/man/oneNN.Rd
ssc/man/predict.coBC.Rd
ssc/man/predict.democratic.Rd
ssc/man/snnrce.Rd
ssc/man/predict.snnrce.Rd
ssc/man/bClassifOneNN.Rd
ssc/man/statistics.Rd
ssc/man/bClassif.Rd
ssc/man/predict.triTraining.Rd
ssc/man/coffee.Rd
ssc/man/setred.Rd
ssc/man/selfTraining.Rd
ssc/man/predict.OneNN.Rd