Multilabel Classification with R Package mlr
This work provides a standardized framework for multilabel classification in R, which is useful for researchers and practitioners in data science and machine learning, but it is incremental as it integrates existing methods into an existing package.
The authors implemented several multilabel classification algorithms in the R package mlr, including binary relevance and classifier chains, and evaluated their performance in a benchmark experiment with multiple datasets.
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.