The R Journal: article published in 2018, volume 10:2

The utiml Package: Multi-label Classification in R
Adriano Rivolli and Andre C. P. L. F. de Carvalho , The R Journal (2018) 10:2, pages 24-37.

Abstract 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.

Received: 2017-04-07; online 2018-08-17, supplementary material, (1.3 KiB)
CRAN packages: mldr, mlr, MLPUGS, utiml, randomForest, C50, e1071, parallel
CRAN Task Views implied by cited CRAN packages: MachineLearning, Environmetrics, Cluster, Distributions, MissingData, Multivariate, Psychometrics

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-041,
  author = {Adriano Rivolli and Andre C. P. L. F. de Carvalho},
  title = {{The utiml Package: Multi-label Classification in R}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-041},
  url = {https://doi.org/10.32614/RJ-2018-041},
  pages = {24--37},
  volume = {10},
  number = {2}
}