imdb

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

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 120919
Attributes 1029
Inputs 1001
Labels 28
Labelsets 4503
Single labelsets 2263
Max frequency 13144
Cardinality 1.9997
Density 0.0714
Mean IR 25.124
SCUMBLE 0.1082
TCS 18.6535

Citation

Read, J.; Pfahringer, B.; Holmes, G.; Frank, E. (2011). Classifier chains for multi-label classification. In Machine Learning, 85(), 333--359.
@article{,
  title = "Classifier chains for multi-label classification",
  author = "Read, J. and Pfahringer, B. and Holmes, G. and Frank, E.",
  journal = "Machine Learning",
  year = "2011",
  volume="85",
  issue="3",
  pages = "333--359"
}

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.