utiml (version 0.1.4)

ecc: Ensemble of Classifier Chains for multi-label Classification

Description

Create an Ensemble of Classifier Chains model for multilabel classification.

Usage

ecc(mdata, base.algorithm = getOption("utiml.base.algorithm", "SVM"),
  m = 10, subsample = 0.75, attr.space = 0.5, replacement = TRUE, ...,
  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 Classifier Chains models used in the ensemble. (Default: 10)

subsample

A value between 0.1 and 1 to determine the percentage of training instances that must be used for each classifier. (Default: 0.75)

attr.space

A value between 0.1 and 1 to determine the percentage of attributes that must be used for each classifier. (Default: 0.50)

replacement

Boolean value to define if use sampling with replacement to create the data of the models of the ensemble. (Default: TRUE)

...

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))

Value

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

rounds

The number of interactions

models

A list of BR models.

nrow

The number of instances used in each training dataset

ncol

The number of attributes used in each training dataset

Details

This model is composed by a set of Classifier Chains models. Classifier Chains is a Binary Relevance transformation method based to predict multi-label data. It is different from BR method due the strategy of extended the attribute space with the 0/1 label relevances of all previous classifiers, forming a classifier chain.

References

Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333-359.

Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2009). Classifier Chains for Multi-label Classification. Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, 5782, 254-269.

See Also

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

Other Ensemble methods: ebr, eps

Examples

# NOT RUN {
# Use all default values
model <- ecc(toyml, "RANDOM")
pred <- predict(model, toyml)

# }
# NOT RUN {
# Use J48 with 100% of instances and only 5 rounds
model <- ecc(toyml, 'J48', m = 5, subsample = 1)

# Use 75% of attributes
model <- ecc(toyml, attr.space = 0.75)

# Running in 4 cores and define a specific seed
model1 <- ecc(toyml, cores=4, seed=123)
# }