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.
mldr, rFerns, randomForestSRC, randomForestSRC, ada, batchtools
HighPerformanceComputing, MachineLearning, Survival
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For attribution, please cite this work as
Probst, et al., "Multilabel Classification with R Package mlr", The R Journal, 2017
BibTeX citation
@article{RJ-2017-012,
author = {Probst, Philipp and Au, Quay and Casalicchio, Giuseppe and Stachl, Clemens and Bischl, Bernd},
title = {Multilabel Classification with R Package mlr},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-012},
doi = {10.32614/RJ-2017-012},
volume = {9},
issue = {1},
issn = {2073-4859},
pages = {352-369}
}