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) .

Documentation

Manual: utiml.pdf
Vignette: utiml: Utilities for Multi-label Learning

Maintainer: Adriano Rivolli <rivolli at utfpr.edu.br>

Author(s): Adriano Rivolli*

Install package and any missing dependencies by running this line in your R console:

install.packages("utiml")

Depends R (>= 3.0.0), mldr(>=0.3.22)
Imports stats, utils
Suggests C50, e1071, FSelector, infotheo, kknn, knitr, parallel, randomForest, rJava(>=0.9), rmarkdown, rpart, RWeka(>=0.4), testthat, xgboost
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Package utiml
Materials
URL https://github.com/rivolli/utiml
Task Views
Version 0.1.2
Published 2017-04-06
License GPL | file LICENSE
BugReports https://github.com/rivolli/utiml
SystemRequirements
NeedsCompilation no
Citation
CRAN checks utiml check results
Package source utiml_0.1.2.tar.gz