rpc: Ranking by Pairwise Comparison (RPC) for multi-label...

In utiml: Utilities for Multi-Label Learning

Description

Create a RPC model for multilabel classification.

Usage

rpc(
  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: 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))

Details

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.

Value

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.

References

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

See Also

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

Other Pairwise methods: clr()

Examples

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

utiml documentation built on May 31, 2021, 9:09 a.m.