ps {utiml}

Pruned Set for multi-label Classification

Description

Create a Pruned Set model for multilabel classification.

Usage

ps(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  p = 3,
  strategy = c("A", "B"),
  b = 2,
  ...,
  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)

strategy

The strategy (A or B) for processing infrequent labelsets. (Default: A).

b

The number used by the strategy for processing infrequent labelsets.

...

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 Set (PS) is a multi-class transformation that remove the less common classes to predict multi-label data.

Value

An object of class PSmodel 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).

See Also

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

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

Examples

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


##Change default configurations
model <- ps(toyml, "RF", p=4, strategy="B", b=1)