Democratic is a semi-supervised learning algorithm with a co-training
style. This algorithm trains N classifiers with different learning schemes defined in
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
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
democraticG can be helpful in those cases where the method selected as
base classifier needs a
pred functions with other
specifications. For more information about the general democratic method,
democratic function. Essentially,
function is a wrapper of
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
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
List of three vectors with the same information in
but the indexes are relative to
The levels of
Yan Zhou and Sally Goldman.
In IEEE 16th International Conference on Tools with Artificial Intelligence (ICTAI), pages 594-602. IEEE, Nov 2004. doi: 10.1109/ICTAI.2004.48.
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