GnegativeGO

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

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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 1392
Attributes 1725
Inputs 1717
Labels 8
Labelsets 19
Single labelsets 5
Max frequency 533
Cardinality 1.046
Density 0.1307
Mean IR 18.4476
SCUMBLE 0.0096
TCS 12.4722

Citation

Xu, Jianhua; Liu, Jiali; Yin, Jing; Sun, Chengyu (2016). A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously. In Knowledge-Based Systems, 98(), 172--184.
@article{,
  title={A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously},
  author={Xu, Jianhua and Liu, Jiali and Yin, Jing and Sun, Chengyu},
  journal={Knowledge-Based Systems},
  volume={98},
  pages={172--184},
  year={2016},
  publisher={Elsevier}
}

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.