The R Journal: article published in 2017, volume 9:1

Multilabel Classification with R Package mlr PDF download
Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl and Bernd Bischl , The R Journal (2017) 9:1, pages 352-369.

Abstract We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of randomForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.

Received: 2016-09-12; online 2017-05-10
CRAN packages: mldr, rFerns, randomForestSRC, randomForestSRC, ada, batchtools
CRAN Task Views implied by cited CRAN packages: HighPerformanceComputing, MachineLearning, Survival

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Philipp Probst and Quay Au and Giuseppe Casalicchio and
          Clemens Stachl and Bernd Bischl},
  title = {{Multilabel Classification with R Package mlr}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-012},
  url = {},
  pages = {352--369},
  volume = {9},
  number = {1}