corel16k010

mldr.datasets::get.mldr("corel16k010")

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 13618
Attributes 644
Inputs 500
Labels 144
Labelsets 4692
Single labelsets 2999
Max frequency 350
Cardinality 2.8153
Density 0.0196
Mean IR 32.9985
SCUMBLE 0.2786
TCS 19.638

Citation

Barnard, K.; Duygulu, P.; Forsyth, D.; de Freitas, N.; Blei, D. M.; Jordan, M. I. (2003). Matching words and pictures. In Journal of Machine Learning Research, 3(), 1107--1135.
@article{,
    title = "Matching words and pictures",
    author = "Barnard, K. and Duygulu, P. and Forsyth, D. and de Freitas, N. and Blei, D. M. and Jordan, M. I.",
    journal = "Journal of Machine Learning Research",
    year = "2003",
    volume = "3",
    pages = "1107--1135",
}

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.