Statistics, Department of

 

The R Journal

Date of this Version

6-2017

Document Type

Article

Citation

The R Journal (June 2017) 9(1); Editor: Roger Bivand

Comments

Copyright 2017, The R Foundation. Open access material. License: CC BY 4.0 International

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 random ForestSRC 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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