utiml (version 0.1.7)

esl: Ensemble of Single Label

Description

Create an Ensemble of Single Label model for multilabel classification.

Usage

esl(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  m = 10,
  w = 1,
  ...,
  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: options("utiml.base.algorithm", "SVM"))

m

The number of members used in the ensemble. (Default: 10)

w

The weight given to the choice of the less frequent labels. When it is 0, the labels will be random choose, when it is 1 the complement of the label frequency is used as the probability to choose each label. Values greater than 1 will privilege the less frequent labels. (Default: 1)

...

Others arguments passed to the base algorithm for all subproblems

cores

The number of cores to parallelize the training. (Default: options("utiml.cores", 1))

seed

An optional integer used to set the seed. This is useful when the method is run in parallel. (Default: options("utiml.seed", NA))

Value

An object of class ESLmodel containing the set of fitted models, including:

labels

A vector with the labels' frequencies.

models

A list of the multi-class models.

Details

ESL is an ensemble of multi-class model that uses the less frequent labels. This is based on the label ignore approach different members of the ensemble.

See Also

Other Transformation methods: brplus(), br(), cc(), clr(), dbr(), ebr(), ecc(), eps(), homer(), lift(), lp(), mbr(), ns(), ppt(), prudent(), ps(), rakel(), rdbr(), rpc()

Examples

# NOT RUN {
model <- esl(toyml, "RANDOM")
pred <- predict(model, toyml)

# }
# NOT RUN {
# Use SVM as base algorithm
model <- esl(toyml, "SVM")
pred <- predict(model, toyml)

# Change the base algorithm and use 2 CORES
model <- esl(toyml[1:50], 'RF', cores = 2, seed = 123)

# Set a parameters for all subproblems
model <- esl(toyml, 'KNN', k=5)
# }