br: Binary Relevance for multi-label Classification

In utiml: Utilities for Multi-Label Learning

Description

Create a Binary Relevance model for multilabel classification.

Usage

br(
  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. (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

Binary Relevance is a simple and effective transformation method to predict multi-label data. This is based on the one-versus-all approach to build a specific model for each label.

Value

An object of class BRmodel 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

Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757-1771.

See Also

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

Examples

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


# Use SVM as base algorithm
model <- br(toyml, "SVM")
pred <- predict(model, toyml)

# Change the base algorithm and use 2 CORES
model <- br(toyml[1:50], 'RF', cores = 2, seed = 123)

# Set a parameters for all subproblems
model <- br(toyml, 'KNN', k=5)

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