BR+ or BRplus for multi-label Classification
Create a BR+ classifier to predic multi-label data. This is a simple approach that enables the binary classifiers to discover existing label dependency by themselves. The main idea of BR+ is to increment the feature space of the binary classifiers to let them discover existing label dependency by themselves.
A mldr dataset used to train the binary models.
A string with the name of the base method. (Default:
Others arguments passed to the base method for all subproblems.
The number of cores to parallelize the training. Values higher
than 1 require the parallel package. (Default:
An optional integer used to set the seed. This is useful when
the method is run in parallel. (Default:
This implementation has different strategy to predict the final set of labels for unlabeled examples, as proposed in original paper.
An object of class
BRPmodel containing the set of fitted
The label frequencies to use with the 'Stat' strategy
The BR model to predict the values for the labels to initial step
A list of final models named by the label names.
Cherman, E. A., Metz, J., & Monard, M. C. (2012). Incorporating label dependency into the binary relevance framework for multi-label classification. Expert Systems with Applications, 39(2), 1647-1655.
Other Stacking methods:
Other Transformation methods:
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# Use SVM as base method model <- brplus(toyml, "RANDOM") pred <- predict(model, toyml) ## Not run: # Use Random Forest as base method and 4 cores model <- brplus(toyml, 'RF', cores = 4, seed = 123) ## End(Not run)
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