Democratic is a semi-supervised learning algorithm with a co-training
style. This algorithm trains N classifiers with different learning schemes defined in
list gen.learners. During the iterative process, the multiple classifiers with
different inductive biases label data for each other.
democraticG(y, gen.learners, gen.preds)A vector with the labels of training instances. In this vector the
unlabeled instances are specified with the value NA.
A list of functions for training N different supervised base classifiers. Each function needs two parameters, indexes and cls, where indexes indicates the instances to use and cls specifies the classes of those instances.
A list of functions for predicting the probabilities per classes.
Each function must be two parameters, model and indexes, where the model
is a classifier trained with gen.learner function and
indexes indicates the instances to predict.
A list object of class "democraticG" containing:
A vector with the confidence-weighted vote assigned to each classifier.
A list with the final N base classifiers trained using the enlarged labeled set.
List of N vectors of indexes related to the training instances
used per each classifier. These indexes are relative to the y argument.
The indexes of all training instances used to
train the N models. These indexes include the initial labeled instances
and the newly labeled instances. These indexes are relative to the y argument.
List of three vectors with the same information in model.index
but the indexes are relative to instances.index vector.
The levels of y factor.
democraticG can be helpful in those cases where the method selected as
base classifier needs a learner and pred functions with other
specifications. For more information about the general democratic method,
please see democratic function. Essentially, democratic
function is a wrapper of democraticG function.
Yan Zhou and Sally Goldman. Democratic co-learning. In IEEE 16th International Conference on Tools with Artificial Intelligence (ICTAI), pages 594-602. IEEE, Nov 2004. doi: 10.1109/ICTAI.2004.48.