PlantPseAAC

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

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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	978
Attributes	452
Inputs	440
Labels	12
Labelsets	32
Single labelsets	8
Max frequency	277
Cardinality	1.0787
Density	0.0899
Mean IR	6.6904
SCUMBLE	0.0058
TCS	12.0374

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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(252 more attributes)

Label1 SCUMBLE 0.0144 Label2 SCUMBLE 0.0382 Label3 SCUMBLE 0.0036 Label4 SCUMBLE 0.0228 Label5 SCUMBLE 0.0257 Label6 SCUMBLE 0.0039 Label7 SCUMBLE 0.0528 Label8 SCUMBLE 0.0033 Label9 SCUMBLE 0.0038 Label10 SCUMBLE 0 Label11 SCUMBLE 0.018 Label12 SCUMBLE 0.0169

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