ppt {utiml}

Pruned Problem Transformation for multi-label Classification

Description

Create a Pruned Problem Transformation model for multilabel classification.

Usage

ppt(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  p = 3,
  info.loss = FALSE,
  ...,
  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"))

p

Number of instances to prune. All labelsets that occurs p times or less in the training data is removed. (Default: 3)

info.loss

Logical value where TRUE means discard infrequent labelsets and FALSE means reintroduce infrequent labelsets via subsets. (Default: FALSE)

...

Others arguments passed to the base algorithm for all subproblems

cores

Not used

seed

An optional integer used to set the seed. (Default: options("utiml.seed", NA))

Details

Pruned Problem Transformation (PPT) is a multi-class transformation that remove the less common classes to predict multi-label data.

Value

An object of class PPTmodel containing the set of fitted models, including:

labels

A vector with the label names.

model

A LP model contained only the most common labelsets.

References

Read, J., Pfahringer, B., & Holmes, G. (2008). Multi-label classification using ensembles of pruned sets. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 995–1000). Read, J. (2008). A pruned problem transformation method for multi-label classification. In Proceedings of the New Zealand Computer Science Research Student Conference (pp. 143-150).

See Also

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

Other Powerset: eps(), lp(), ps(), rakel()

Examples

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


##Change default configurations
model <- ppt(toyml, "RF", p=4, info.loss=TRUE)