mediamill

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

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 43907
Attributes 221
Inputs 120
Labels 101
Labelsets 6555
Single labelsets 4104
Max frequency 2363
Cardinality 4.3756
Density 0.0433
Mean IR 256.4047
SCUMBLE 0.3547
TCS 18.1906

Citation

Snoek, C. G. M.; Worring, M.; van Gemert, J. C.; Geusebroek, J. M.; Smeulders, A. W. M. (2006). The challenge problem for automated detection of 101 semantic concepts in multimedia.
@inproceedings{,
  title = "The challenge problem for automated detection of 101 semantic concepts in multimedia",
  author = "Snoek, C. G. M. and Worring, M. and van Gemert, J. C. and Geusebroek, J. M. and Smeulders, A. W. M.",
  booktitle = "Proc. 14th ACM International Conference on Multimedia, MULTIMEDIA06,
  year = "2006",
  pages = "421--430"
}

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.