The R Journal: accepted article

This article will be copy edited and may be changed before publication.

Multilabel Classification with R Package mlr
Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl and Bernd Bischl

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}},
  url = {}