democraticCombine

A list with the prediction for each classifier.

pred

A vector with the confidence-weighted vote assigned to each classifier 
during the training process.

classes

This function combines the probabilities predicted by the set of 
classifiers.

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.

Christoph Bergmeir

Semi-Supervised Classification Methods

democraticCombine: Combining the hypothesis of the classifiers

Description

Usage

Arguments

Value