utiml (version 0.1.7)

rdbr: Recursive Dependent Binary Relevance (RDBR) for multi-label Classification

Description

Create a RDBR classifier to predict multi-label data. This is a recursive approach that enables the binary classifiers to discover existing label dependency by themselves. The idea of RDBR is running DBR recursively until the results stabilization of the result.

Usage

rdbr(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  estimate.models = TRUE,
  ...,
  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"))

estimate.models

Logical value indicating whether is necessary build Binary Relevance classifier for estimate process. The default implementation use BR as estimators, however when other classifier is desirable then use the value FALSE to skip this process. (Default: TRUE).

...

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 RDBRmodel containing the set of fitted models, including:

labels

A vector with the label names.

estimation

The BR model to estimate the values for the labels. Only when the estimate.models = TRUE.

models

A list of final models named by the label names.

Details

The train method is exactly the same of DBR the recursion is in the predict method.

References

Rauber, T. W., Mello, L. H., Rocha, V. F., Luchi, D., & Varejao, F. M. (2014). Recursive Dependent Binary Relevance Model for Multi-label Classification. In Advances in Artificial Intelligence - IBERAMIA, 206-217.

See Also

Dependent Binary Relevance (DBR)

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

Examples

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

# }
# NOT RUN {
# Use Random Forest as base algorithm and 2 cores
model <- rdbr(toyml, 'RF', cores = 2, seed = 123)
# }