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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: | 0.1.7 |
Depends: | R (≥ 3.0.0), mldr (≥ 0.4.0), parallel, ROCR |
Imports: | stats, utils, methods |
Suggests: | C50, e1071, infotheo, kknn, knitr, randomForest, rmarkdown, markdown, rpart, testthat, xgboost (≥ 0.6-4) |
Published: | 2021-05-31 |
DOI: | 10.32614/CRAN.package.utiml |
Author: | Adriano Rivolli [aut, cre] |
Maintainer: | Adriano Rivolli <rivolli at utfpr.edu.br> |
BugReports: | https://github.com/rivolli/utiml |
License: | GPL-3 |
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_0.1.7.tar.gz |
Windows binaries: | r-devel: utiml_0.1.7.zip, r-release: utiml_0.1.7.zip, r-oldrel: utiml_0.1.7.zip |
macOS binaries: | r-release (arm64): utiml_0.1.7.tgz, r-oldrel (arm64): utiml_0.1.7.tgz, r-release (x86_64): utiml_0.1.7.tgz, r-oldrel (x86_64): utiml_0.1.7.tgz |
Old sources: | utiml archive |
Please use the canonical form https://CRAN.R-project.org/package=utiml to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.