bookmarks

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

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 87856
Attributes 2358
Inputs 2150
Labels 208
Labelsets 18716
Single labelsets 14971
Max frequency 6087
Cardinality 2.0281
Density 0.0098
Mean IR 12.308
SCUMBLE 0.0597
TCS 22.8479

Citation

Katakis, I.; Tsoumakas, G.; Vlahavas, I. (2008). Multilabel Text Classification for Automated Tag Suggestion. In Proc. ECML PKDD08 Discovery Challenge, Antwerp, Belgium, 75-83.
@inproceedings{,
  author = "Katakis, I. and Tsoumakas, G. and Vlahavas, I.",
  title = "Multilabel Text Classification for Automated Tag Suggestion",
  booktitle = "Proc. ECML PKDD08 Discovery Challenge, Antwerp, Belgium",
  pages = "75-83",
  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.