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arXiv · 1703.08991

Multilabel Classification with R Package mlr

Philipp Probst·Quay Au·Giuseppe Casalicchio·Clemens Stachl·Bernd Bischl
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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.

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Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl. 2017-03-27. Multilabel Classification with R Package mlr. https://doi.org/10.32614/rj-2017-012

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