utiml: 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) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and Ventura, S. (2015) A Tutorial on Multi-label Learning.

Version: 0.1.6
Depends: R (≥ 3.0.0), mldr (≥ 0.4.0), parallel, ROCR
Imports: stats, utils, methods
Suggests: C50, e1071, FSelector, infotheo, kknn, knitr, randomForest, rJava (≥ 0.9), rmarkdown, rpart, RWeka (≥ 0.4), testthat, xgboost (≥ 0.6-4)
Published: 2020-02-07
Author: Adriano Rivolli [aut, cre]
Maintainer: Adriano Rivolli <rivolli at utfpr.edu.br>
BugReports: https://github.com/rivolli/utiml
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL | file LICENSE]
URL: https://github.com/rivolli/utiml
NeedsCompilation: no
Materials: README NEWS
CRAN checks: utiml results


Reference manual: utiml.pdf
Vignettes: utiml: Utilities for Multi-label Learning
Package source: utiml_0.1.6.tar.gz
Windows binaries: r-prerelease: utiml_0.1.6.zip, r-release: utiml_0.1.6.zip, r-oldrel: utiml_0.1.6.zip
macOS binaries: r-prerelease: utiml_0.1.6.tgz, r-release: utiml_0.1.6.tgz, r-oldrel: utiml_0.1.6.tgz
Old sources: utiml archive


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