lift {utiml}

LIFT for multi-label Classification

Description

Create a multi-label learning with Label specIfic FeaTures (LIFT) model.

Usage

lift(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  ratio = 0.1,
  ...,
  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"))

ratio

Control the number of clusters being retained. Must be between 0 and 1. (Default: 0.1)

...

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. This is useful when the method is run in parallel. (Default: options("utiml.seed", NA))

Details

LIFT firstly constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results.

Value

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

labels

A vector with the label names.

models

A list of the generated models, named by the label names.

References

Zhang, M.-L., & Wu, L. (2015). Lift: Multi-Label Learning with Label-Specific Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(1), 107-120.

See Also

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

Examples

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


# Runing lift with a specific ratio
model <- lift(toyml, "RF", 0.15)