EukaryotePseAAC

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

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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	7766
Attributes	462
Inputs	440
Labels	22
Labelsets	112
Single labelsets	37
Max frequency	1580
Cardinality	1.1456
Density	0.0521
Mean IR	45.0117
SCUMBLE	0.0174
TCS	13.8963

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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(262 more attributes)

Label1 SCUMBLE 0.1513 Label2 SCUMBLE 0.0401 Label3 SCUMBLE 0.1314 Label4 SCUMBLE 0.1699 Label5 SCUMBLE 0.0009 Label6 SCUMBLE 0 Label7 SCUMBLE 0.0336 Label8 SCUMBLE 0.1276 Label9 SCUMBLE 0.0182 Label10 SCUMBLE 0.1536 Label11 SCUMBLE 0.0097 Label12 SCUMBLE 0.0502 Label13 SCUMBLE 0 Label14 SCUMBLE 0.0323 Label15 SCUMBLE 0.1441 Label16 SCUMBLE 0.1919 Label17 SCUMBLE 0.0206 Label18 SCUMBLE 0.0167 Label19 SCUMBLE 0.0601 Label20 SCUMBLE 0.2758 Label21 SCUMBLE 0.3199 Label22 SCUMBLE 0.0292

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