Utilities for Multi-Label Learning

Multi-label learning strategies and others procedures to support multi- label classification in R. The package provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. A complete overview of the matter can be seen in Zhang, M. and Zhou, Z. (2014) and Gibaja, E. and Ventura, S. (2015) .

The utiml package is a framework to support multi-label processing, like Mulan on Weka.

The main methods available on this package are organized in the groups:

• Classification methods
• Evaluation methods
• Pre-process utilities
• Sampling methods
• Threshold methods

Instalation

The installation process is similar to other packages available on CRAN:

install.packages("utiml")

This will also install mldr. To run the examples in this document, you also need to install the packages:

# Base classifiers (SVM and Random Forest)
install.packages(c("e1071", "randomForest"))

Install via github (development version)

devtools::install_github("rivolli/utiml")

Multi-label Classification

Running Binary Relevance Method

library(utiml)
 
# Create two partitions (train and test) of toyml multi-label dataset
ds <- create_holdout_partition(toyml, c(train=0.65, test=0.35))
 
# Create a Binary Relevance Model using e1071::svm method
brmodel <- br(ds$train, "SVM", seed=123)
 
# Predict
prediction <- predict(brmodel, ds$test)
 
# Show the predictions
head(as.bipartition(prediction))
head(as.ranking(prediction))
 
# Apply a threshold
newpred <- rcut_threshold(prediction, 2)
 
# Evaluate the models
result <- multilabel_evaluate(ds$tes, prediction, "bipartition")
thresres <- multilabel_evaluate(ds$tes, newpred, "bipartition")
 
# Print the result
print(round(cbind(Default=result, RCUT=thresres), 3))

Running Ensemble of Classifier Chains

library(utiml)
 
# Create three partitions (train, val, test) of emotions dataset
partitions <- c(train = 0.6, val = 0.2, test = 0.2)
ds <- create_holdout_partition(emotions, partitions, method="iterative")
 
# Create an Ensemble of Classifier Chains using Random Forest (randomForest package)
eccmodel <- ecc(ds$train, "RF", m=3, cores=parallel::detectCores(), seed=123)
 
# Predict
val <- predict(eccmodel, ds$val, cores=parallel::detectCores())
test <- predict(eccmodel, ds$test, cores=parallel::detectCores())
 
# Apply a threshold
thresholds <- scut_threshold(val, ds$val, cores=parallel::detectCores())
new.val <- fixed_threshold(val, thresholds)
new.test <- fixed_threshold(test, thresholds)
 
# Evaluate the models
measures <- c("subset-accuracy", "F1", "hamming-loss", "macro-based") 
 
result <- cbind(
  Test = multilabel_evaluate(ds$tes, test, measures),
  TestWithThreshold = multilabel_evaluate(ds$tes, new.test, measures),
  Validation = multilabel_evaluate(ds$val, val, measures),
  ValidationWithThreshold = multilabel_evaluate(ds$val, new.val, measures)
)
 
print(round(result, 3))

More examples and details are available on functions documentations and vignettes, please refer to the documentation.