langlog

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

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 1460
Attributes 1079
Inputs 1004
Labels 75
Labelsets 304
Single labelsets 189
Max frequency 207
Cardinality 1.1801
Density 0.0157
Mean IR 39.2669
SCUMBLE 0.051
TCS 16.9463

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.