reutersk500

mldr.datasets::get.mldr("reutersk500")

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 6000
Attributes 603
Inputs 500
Labels 103
Labelsets 811
Single labelsets 513
Max frequency 381
Cardinality 1.4622
Density 0.0142
Mean IR 51.9805
SCUMBLE 0.0517
TCS 17.5476

Citation

Read, Jesse (2010). Scalable multi-label classification.
@phdthesis{,
  title="Scalable multi-label classification",
  author="Read, Jesse",
  year="2010",
  school="University of Waikato"
}

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.