utiml (version 0.1.7)

mbr: Meta-BR or 2BR for multi-label Classification

Description

Create a Meta-BR (MBR) classifier to predict multi-label data. To this, two round of Binary Relevance is executed, such that, the first step generates new attributes to enrich the second prediction.

Usage

mbr(
  mdata,
  base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  folds = 1,
  phi = 0,
  ...,
  predict.params = list(),
  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"))

folds

The number of folds used in internal prediction. If this value is 1 all dataset will be used in the first prediction. (Default: 1)

phi

A value between 0 and 1 to determine the correlation coefficient, The value 0 include all labels in the second phase and the 1 only the predicted label. (Default: 0)

...

Others arguments passed to the base algorithm for all subproblems.

predict.params

A list of default arguments passed to the predictor algorithm. (Default: list())

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))

Value

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

labels

A vector with the label names.

phi

The value of phi parameter.

correlation

The matrix of label correlations used in combination with phi parameter to define the labels used in the second step.

basemodel

The BRModel used in the first iteration.

models

A list of models named by the label names used in the second iteration.

Details

This implementation use complete training set for both training and prediction steps of 2BR. However, the phi parameter may be used to remove labels with low correlations on the second step.

References

Tsoumakas, G., Dimou, A., Spyromitros, E., Mezaris, V., Kompatsiaris, I., & Vlahavas, I. (2009). Correlation-based pruning of stacked binary relevance models for multi-label learning. In Proceedings of the Workshop on Learning from Multi-Label Data (MLD'09) (pp. 22-30). Godbole, S., & Sarawagi, S. (2004). Discriminative Methods for Multi-labeled Classification. In Data Mining and Knowledge Discovery (pp. 1-26).

See Also

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

Other Stacking methods: brplus()

Examples

# NOT RUN {
model <- mbr(toyml, "RANDOM")
pred <- predict(model, toyml)

# }
# NOT RUN {
# Use 10 folds and different phi correlation with C5.0 classifier
model <- mbr(toyml, 'C5.0', 10, 0.2)

# Run with 2 cores
 model <- mbr(toyml, "SVM", cores = 2, seed = 123)

# Set a specific parameter
model <- mbr(toyml, 'KNN', k=5)
# }