eurlexev

mldr.datasets::get.mldr("eurlexev")

Select your download

Partitions: select your desired partitioning strategy, validation and format

Random Stratified Iterative stratified
Hold out MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
2x5-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr
10-fold cross validation MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr MULAN MEKA LibSVM KEEL mldr

Summary

Instances 19348
Attributes 8993
Inputs 5000
Labels 3993
Labelsets 16467
Single labelsets 14609
Max frequency 34
Cardinality 5.3102
Density 0.0013
Mean IR 396.636
SCUMBLE 0.4201
TCS 26.5186

Citation

Mencia, E. L.; F{ (2008). Efficient pairwise multilabel classification for large-scale problems in the legal domain. In Machine Learning and Knowledge Discovery in Databases, 50--65.
@incollection{,
  title="Efficient pairwise multilabel classification for large-scale problems in the legal domain",
  author="Mencia, E. L. and F{"u}rnkranz, J.",
  booktitle="Machine Learning and Knowledge Discovery in Databases",
  pages="50--65",
  year="2008"
}

Concurrence plot

In this concurrence plot, sectors represent labels and links between them depict label co-occurrences. SCUMBLE is a measure designed to assess the concurrence among imbalanced labels.