bibtex

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

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 7395
Attributes 1995
Inputs 1836
Labels 159
Labelsets 2856
Single labelsets 2199
Max frequency 471
Cardinality 2.4019
Density 0.0151
Mean IR 12.4983
SCUMBLE 0.0938
TCS 20.5414

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.