emotions 

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

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 593
Attributes 78
Inputs 72
Labels 6
Labelsets 27
Single labelsets 4
Max frequency 81
Cardinality 1.8685
Density 0.3114
Mean IR 1.4781
SCUMBLE 0.011
TCS 9.3643

Citation

Text

Wieczorkowska, A.; Synak, P.; Ra'{s (2006). Multi-Label Classification of Emotions in Music. In Intelligent Information Processing and Web Mining, 307--315.

BibTeX

@incollection{,
  title = "Multi-Label Classification of Emotions in Music",
  author = "Wieczorkowska, A. and Synak, P. and Ra'{s}, Z.",
  booktitle = "Intelligent Information Processing and Web Mining",
  year = "2006",
  volume = "35",
  chapter = "30",
  pages = "307--315"
}

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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