Semi-Supervised Classification Methods
CoBC method
Combining the hypothesis
CoBC generic method
Democratic method
Combining the hypothesis of the classifiers
Democratic generic method
1-NN supervised classifier builder
Predictions of the coBC method
Predictions of the Democratic method
Model Predictions
Predictions of the Self-training method
Predictions of the SETRED method
Predictions of the SNNRCE method
Predictions of the Tri-training method
Self-training method
Self-training generic method
SETRED method
SETRED generic method
SNNRCE method
Tri-training method
Combining the hypothesis
Tri-training generic method
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.