clr: Calibrated Label Ranking (CLR) for multi-label Classification

In utiml: Utilities for Multi-Label Learning

Description

Create a CLR model for multilabel classification.

Usage

clr(
  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

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.

Value

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.

References

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.

See Also

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

Other Pairwise methods: rpc()

Examples

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

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