lp {utiml} | R Documentation |
Label Powerset for multi-label Classification
Description
Create a Label Powerset model for multilabel classification.
Usage
lp(
mdata,
base.algorithm = getOption("utiml.base.algorithm", "SVM"),
...,
cores = getOption("utiml.cores", 1),
seed = getOption("utiml.seed", NA)
)
Arguments
mdata |
A mldr dataset used to train the binary models. |
base.algorithm |
A string with the name of the base algorithm. (Default:
|
... |
Others arguments passed to the base algorithm for all subproblems |
cores |
Not used |
seed |
An optional integer used to set the seed. (Default:
|
Details
Label Powerset is a simple transformation method to predict multi-label data. This is based on the multi-class approach to build a model where the classes are each labelset.
Value
An object of class LPmodel
containing the set of fitted
models, including:
- labels
A vector with the label names.
- model
A multi-class model.
References
Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757-1771.
See Also
Other Transformation methods:
brplus()
,
br()
,
cc()
,
clr()
,
dbr()
,
ebr()
,
ecc()
,
eps()
,
esl()
,
homer()
,
lift()
,
mbr()
,
ns()
,
ppt()
,
prudent()
,
ps()
,
rakel()
,
rdbr()
,
rpc()
Other Powerset:
eps()
,
ppt()
,
ps()
,
rakel()
Examples
model <- lp(toyml, "RANDOM")
pred <- predict(model, toyml)