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: | 0.1.5 |
| Depends: | R (≥ 3.0.0), mldr (≥ 0.4.0), parallel, ROCR |
| Imports: | stats, utils |
| Suggests: | C50, e1071, FSelector, infotheo, kknn, knitr, randomForest, rJava (≥ 0.9), rmarkdown, rpart, RWeka (≥ 0.4), testthat, xgboost (≥ 0.6-4) |
| Published: | 2019-03-16 |
| 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_0.1.5.tar.gz |
| Windows binaries: | r-devel: utiml_0.1.5.zip, r-devel-gcc8: utiml_0.1.5.zip, r-release: utiml_0.1.5.zip, r-oldrel: utiml_0.1.5.zip |
| OS X binaries: | r-release: utiml_0.1.5.tgz, r-oldrel: utiml_0.1.5.tgz |
| Old sources: | utiml archive |
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