GpositiveGO

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

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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	519
Attributes	916
Inputs	912
Labels	4
Labelsets	7
Single labelsets	2
Max frequency	206
Cardinality	1.0077
Density	0.2519
Mean IR	3.8605
SCUMBLE	0.001
TCS	10.1478

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.

Attributes

all attributes labels

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(716 more attributes)

Label1 SCUMBLE 0.0025 Label2 SCUMBLE 0.0232 Label3 SCUMBLE 0.0003 Label4 SCUMBLE 0.0007

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