Learning classification tasks in which each instance is associated with one or more labels are known as multi-label learning. The implementation of multi-label algorithms, performed by different researchers, have several specificities, like input/output format, different internal functions, distinct programming language, to mention just some of them. As a result, current machine learning tools include only a small subset of multi-label decomposition strategies. The utiml package is a framework for the application of classification algorithms to multi-label data. Like the well known MULAN used with Weka, it provides a set of multi-label procedures such as sampling methods, transformation strategies, threshold functions, pre-processing techniques and evaluation metrics. The package was designed to allow users to easily perform complete multi-label classification experiments in the R environment. This paper describes the utiml API and illustrates its use in different multi-label classification scenarios.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-041.zip
mldr, mlr, MLPUGS, utiml, randomForest, C50, e1071, parallel
MachineLearning, Environmetrics, Cluster, Distributions, MissingData, Multivariate, Psychometrics
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Rivolli & Carvalho, "The utiml Package: Multi-label Classification in R", The R Journal, 2018
BibTeX citation
@article{RJ-2018-041,
author = {Rivolli, Adriano and Carvalho, Andre C. P. L. F. de},
title = {The utiml Package: Multi-label Classification in R},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-041},
doi = {10.32614/RJ-2018-041},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {24-37}
}