Description Usage Arguments Details Value References See Also Examples
Create a Meta-BR (MBR) classifier to predict multi-label data. To this, two round of Binary Relevance is executed, such that, the first step generates new attributes to enrich the second prediction.
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mdata |
A mldr dataset used to train the binary models. |
base.algorithm |
A string with the name of the base algorithm. (Default:
|
folds |
The number of folds used in internal prediction. If this value is 1 all dataset will be used in the first prediction. (Default: 1) |
phi |
A value between 0 and 1 to determine the correlation coefficient, The value 0 include all labels in the second phase and the 1 only the predicted label. (Default: 0) |
... |
Others arguments passed to the base algorithm for all subproblems. |
predict.params |
A list of default arguments passed to the predictor
algorithm. (Default: |
cores |
The number of cores to parallelize the training. Values higher
than 1 require the parallel package. (Default:
|
seed |
An optional integer used to set the seed. This is useful when
the method is run in parallel. (Default: |
This implementation use complete training set for both training and
prediction steps of 2BR. However, the phi
parameter may be used to
remove labels with low correlations on the second step.
An object of class MBRmodel
containing the set of fitted
models, including:
A vector with the label names.
The value of phi
parameter.
The matrix of label correlations used in combination
with phi
parameter to define the labels used in the second
step.
The BRModel used in the first iteration.
A list of models named by the label names used in the second iteration.
Tsoumakas, G., Dimou, A., Spyromitros, E., Mezaris, V., Kompatsiaris, I., & Vlahavas, I. (2009). Correlation-based pruning of stacked binary relevance models for multi-label learning. In Proceedings of the Workshop on Learning from Multi-Label Data (MLD'09) (pp. 22-30). Godbole, S., & Sarawagi, S. (2004). Discriminative Methods for Multi-labeled Classification. In Data Mining and Knowledge Discovery (pp. 1-26).
Other Transformation methods:
brplus()
,
br()
,
cc()
,
clr()
,
dbr()
,
ebr()
,
ecc()
,
eps()
,
esl()
,
homer()
,
lift()
,
lp()
,
ns()
,
ppt()
,
prudent()
,
ps()
,
rakel()
,
rdbr()
,
rpc()
Other Stacking methods:
brplus()
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