flags 

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

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 194
Attributes 26
Inputs 19
Labels 7
Labelsets 54
Single labelsets 24
Max frequency 27
Cardinality 3.3918
Density 0.4845
Mean IR 2.2547
SCUMBLE 0.0606
TCS 8.8793

Citation

Text

Goncalves, E. C.; Plastino, A.; Freitas, A. A. (2013). A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on, 469--476.

BibTeX

@inproceedings{,
  title="A genetic algorithm for optimizing the label ordering in multi-label classifier chains",
  author="Goncalves, E. C. and Plastino, A. and Freitas, A. A.",
  booktitle="Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on",
  pages="469--476",
  year="2013",
}

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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