rdbr: Recursive Dependent Binary Relevance (RDBR) for multi-label...
In rivolli/utiml: Utilities for Multi-Label Learning

Description Usage Arguments Details Value References See Also Examples

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.

rdbr(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  estimate.models = TRUE,
  ...,
  cores = getOption("utiml.cores", 1),
  seed = getOption("utiml.seed", NA)
)

`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)`)

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

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.

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.

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()

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


# Use Random Forest as base algorithm and 2 cores
model <- rdbr(toyml, 'RF', cores = 2, seed = 123)