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
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For attribution, please cite this work as
Rivolli & Carvalho, "The R Journal: 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 R Journal: 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}
}