scene

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

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 2407
Attributes 300
Inputs 294
Labels 6
Labelsets 15
Single labelsets 3
Max frequency 405
Cardinality 1.074
Density 0.179
Mean IR 1.2538
SCUMBLE 0.0003
TCS 10.1834

Citation

Boutell, M.; Luo, J.; Shen, X.; Brown, C. (2004). Learning multi-label scene classification. In Pattern Recognition, 37(9), 1757--1771.
@article{,
  title = "Learning multi-label scene classification",
  author = "Boutell, M. and Luo, J. and Shen, X. and Brown, C.",
  journal = "Pattern Recognition",
  year = "2004",
  volume = "37",
  number = "9",
  pages = "1757--1771",
}

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.