Description Usage Arguments Details Value Note References See Also Examples
Create an Ensemble of Binary Relevance model for multilabel classification.
1 2 3 4 5 6 7 8 9 10 11 |
mdata |
A mldr dataset used to train the binary models. |
base.algorithm |
A string with the name of the base algorithm. (Default:
|
m |
The number of Binary Relevance models used in the ensemble. (Default: 10) |
subsample |
A value between 0.1 and 1 to determine the percentage of training instances that must be used for each classifier. (Default: 0.75) |
attr.space |
A value between 0.1 and 1 to determine the percentage of attributes that must be used for each classifier. (Default: 0.50) |
replacement |
Boolean value to define if use sampling with replacement to create the data of the models of the ensemble. (Default: TRUE) |
... |
Others arguments passed to the base algorithm for all subproblems. |
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 model is composed by a set of Binary Relevance models. Binary Relevance is a simple and effective transformation method to predict multi-label data.
An object of class EBRmodel
containing the set of fitted
BR models, including:
A list of BR models.
The number of instances used in each training dataset.
The number of attributes used in each training dataset.
The number of interactions.
If you want to reproduce the same classification and obtain the same result will be necessary set a flag utiml.mc.set.seed to FALSE.
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333-359.
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009). Classifier Chains for Multi-label Classification. Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, 5782, 254-269.
Other Transformation methods:
brplus()
,
br()
,
cc()
,
clr()
,
dbr()
,
ecc()
,
eps()
,
esl()
,
homer()
,
lift()
,
lp()
,
mbr()
,
ns()
,
ppt()
,
prudent()
,
ps()
,
rakel()
,
rdbr()
,
rpc()
Other Ensemble methods:
ecc()
,
eps()
1 2 3 4 5 6 7 8 9 10 11 12 | model <- ebr(toyml, "RANDOM")
pred <- predict(model, toyml)
# Use C5.0 with 90% of instances and only 5 rounds
model <- ebr(toyml, 'C5.0', m = 5, subsample = 0.9)
# Use 75% of attributes
model <- ebr(toyml, attr.space = 0.75)
# Running in 2 cores and define a specific seed
model1 <- ebr(toyml, cores=2, seed = 312)
|
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