eurlexsm

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

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 5201
Inputs 5000
Labels 201
Labelsets 2504
Single labelsets 1182
Max frequency 1041
Cardinality 2.2133
Density 0.011
Mean IR 536.9761
SCUMBLE 0.182
TCS 21.6461

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.