Multilabel Classification with R Package mlr
Journal
R Journal
Type
journal article
Date Issued
2017
Author(s)
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.
Language
English
Refereed
Yes
Publisher
arXiv
Volume
9
Number
1
Official URL
Subject(s)
Division(s)
Eprints ID
264571
File(s)![Thumbnail Image]()
open.access
Name
1703.08991.pdf
Size
366.84 KB
Format
Adobe PDF
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