lp: Label Powerset for multi-label Classification

Description Usage Arguments Details Value References See Also Examples

View source: R/method_lp.R

Description

Create a Label Powerset model for multilabel classification.

Usage

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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, ctrl, dbr, ebr, ecc, eps, esl, homer, lift, mbr, ns, ppt, prudent, ps, rakel, rdbr, rpc

Other Powerset: eps, ppt, ps, rakel

Examples

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

Example output

Loading required package: mldr

utiml documentation built on April 20, 2018, 1:04 a.m.