lp: Label Powerset for multi-label Classification

In utiml: Utilities for Multi-Label Learning

Description

Create a Label Powerset model for multilabel classification.

Usage

lp(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  ...,
  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"))
...	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

Label Powerset is a simple transformation method to predict multi-label data. This is based on the multi-class approach to build a model where the classes are each labelset.

Value

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

labels

A vector with the label names.

model

A multi-class model.

References

Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9), 1757-1771.

See Also

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

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

Examples

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

Example output

Loading required package: mldr

utiml documentation built on May 31, 2021, 9:09 a.m.