clr: Calibrated Label Ranking (CLR) for multi-label Classification
In rivolli/utiml: Utilities for Multi-Label Learning

Description Usage Arguments Details Value References See Also Examples

Create a CLR model for multilabel classification.

clr(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  ...,
  cores = getOption("utiml.cores", 1),
  seed = getOption("utiml.seed", NA)
)

`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")`)
`...`	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 run in parallel. (Default: `options("utiml.seed", NA)`)

CLR is an extension of label ranking that incorporates the calibrated scenario. The introduction of an artificial calibration label, separates the relevant from the irrelevant labels.

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

labels: A vector with the label names.
rpcmodel: A RPC model.
brmodel: A BR model used to calibrated the labels.

Brinker, K., Furnkranz, J., & Hullermeier, E. (2006). A unified model for multilabel classification and ranking. In Proceeding of the ECAI 2006: 17th European Conference on Artificial Intelligence. p. 489-493. Furnkranz, J., Hullermeier, E., Loza Mencia, E., & Brinker, K. (2008). Multilabel classification via calibrated label ranking. Machine Learning, 73(2), 133-153.

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