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: |
0.1.7 |
| 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 |
| 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 |
Documentation:
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