eurlexdc

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

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 5412
Inputs 5000
Labels 412
Labelsets 1615
Single labelsets 717
Max frequency 1633
Cardinality 1.2923
Density 0.0031
Mean IR 268.9297
SCUMBLE 0.048
TCS 21.9253

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.