rpc: Ranking by Pairwise Comparison (RPC) for multi-label...
In rivolli/utiml: Utilities for Multi-Label Learning

Description Usage Arguments Details Value References See Also Examples

Create a RPC model for multilabel classification.

rpc(
  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)`)

RPC is a simple transformation method that uses pairwise classification to predict multi-label data. This is based on the one-versus-one approach to build a specific model for each label combination.

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

labels: A vector with the label names.
models: A list of the generated models, named by the label names.

Hullermeier, E., Furnkranz, J., Cheng, W., & Brinker, K. (2008). Label ranking by learning pairwise preferences. Artificial Intelligence, 172(16-17), 1897-1916.

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