delicious

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

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 16105
Attributes 1483
Inputs 500
Labels 983
Labelsets 15806
Single labelsets 15642
Max frequency 19
Cardinality 19.02
Density 0.0193
Mean IR 71.1338
SCUMBLE 0.532
TCS 22.7734

Citation

Tsoumakas, G.; Katakis, I.; Vlahavas, I. (2008). Effective and Efficient Multilabel Classification in Domains with Large Number of Labels. In Proc. ECML/PKDD Workshop on Mining Multidimensional Data, Antwerp, Belgium, MMD08, 30--44.
@inproceedings{,
  author = "Tsoumakas, G. and Katakis, I. and Vlahavas, I.",
  title = "Effective and Efficient Multilabel Classification in Domains with Large Number of Labels",
  booktitle = "Proc. ECML/PKDD Workshop on Mining Multidimensional Data, Antwerp, Belgium, MMD08",
  pages = "30--44",
  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.