cal500 

mldr.datasets::get.mldr("cal500")

Select your download

Full dataset

MULAN/MEKA (.arff) MULAN (.xml) LibSVM KEEL mldr

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 502
Attributes 242
Inputs 68
Labels 174
Labelsets 502
Single labelsets 502
Max frequency 1
Cardinality 26.0438
Density 0.1497
Mean IR 20.5778
SCUMBLE 0.3372
TCS 15.5972

Citation

Text

Turnbull, Douglas; Barrington, Luke; Torres, David; Lanckriet, Gert (2008). Semantic annotation and retrieval of music and sound effects. In Audio, Speech, and Language Processing, IEEE Transactions on, 16(2), 467--476.

BibTeX

@article{,
  title="Semantic annotation and retrieval of music and sound effects",
  author="Turnbull, Douglas and Barrington, Luke and Torres, David and Lanckriet, Gert",
  journal="Audio, Speech, and Language Processing, IEEE Transactions on",
  volume="16",
  number="2",
  pages="467--476",
  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.

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