utiml (version 0.1.4)

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

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))

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.

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.

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, ctrl, dbr, ebr, ecc, eps, esl, homer, lift, lp, mbr, ns, ppt, prudent, ps, rakel, rdbr

Other Pairwise methods: clr

Examples

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

# }
# NOT RUN {
# }