The R Journal: accepted article

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The utiml Package: Multi-label Classification in R PDF download
Adriano Rivolli and Andre C. P. L. F. de Carvalho

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 Kb)
CRAN packages: mldr, mlr, MLPUGS, utiml, randomForest, C50, e1071, parallel
CRAN Task Views implied by cited CRAN packages: MachineLearning, Environmetrics, Cluster, Distributions, Multivariate, Psychometrics

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

  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 = {}