eps {utiml}

Ensemble of Pruned Set for multi-label Classification

Description

Create an Ensemble of Pruned Set model for multilabel classification.

Usage

eps(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  m = 10,
  subsample = 0.75,
  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"))

m

The number of Pruned Set models used in the ensemble.

subsample

A value between 0.1 and 1 to determine the percentage of training instances that must be used for each classifier. (Default: 0.63)

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

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. (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. The ensemble is created with different subsets of the original multi-label data.

Value

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

rounds

The number of interactions

models

A list of PS models.

References

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(), esl(), homer(), lift(), lp(), mbr(), ns(), ppt(), prudent(), ps(), rakel(), rdbr(), rpc()

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

Other Ensemble methods: ebr(), ecc()

Examples

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


##Change default configurations
model <- eps(toyml, "RF", m=15, subsample=0.4, p=4, strategy="B", b=1)