rakel {utiml}

Random k-labelsets for multilabel classification

Description

Create a RAkEL model for multilabel classification.

Usage

rakel(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  k = 3,
  m = 2 * mdata$measures$num.labels,
  overlapping = TRUE,
  ...,
  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"))

k

The number of labels used in each labelset. (Default: 3)

m

The number of LP models. Used when overlapping is TRUE, otherwise it is ignored. (Default: 2 * length(labels))

overlapping

Logical value, that defines if the method must overlapping the labelsets. If FALSE the method uses disjoint labelsets. (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: options("utiml.cores", 1))

seed

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

Details

RAndom k labELsets is an ensemble of LP models where each classifier is trained with a small set of labels, called labelset. Two different strategies for constructing the labelsets are the disjoint and overlapping labelsets.

Value

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

labels

A vector with the label names.

labelsets

A list with the labelsets used to build the LP models.

model

A list of the generated models, named by the label names.

References

Tsoumakas, G., Katakis, I., & Vlahavas, I. (2011). Random k-labelsets for multilabel classification. IEEE Transactions on Knowledge and Data Engineering, 23(7), 1079-1089.

See Also

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

Other Powerset: eps(), lp(), ppt(), ps()

Examples

model <- rakel(toyml, "RANDOM")
pred <- predict(model, toyml)

## SVM using k = 4 and m = 100
model <- rakel(toyml, "SVM", k=4, m=100)

## Random Forest using disjoint labelsets
model <- rakel(toyml, "RF", overlapping=FALSE)