scene - Cometa

scene

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

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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	2407
Attributes	300
Inputs	294
Labels	6
Labelsets	15
Single labelsets	3
Max frequency	405
Cardinality	1.074
Density	0.179
Mean IR	1.2538
SCUMBLE	0.0003
TCS	10.1834

Citation

Boutell, M.; Luo, J.; Shen, X.; Brown, C. (2004). Learning multi-label scene classification. In Pattern Recognition, 37(9), 1757--1771.

@article{,
  title = "Learning multi-label scene classification",
  author = "Boutell, M. and Luo, J. and Shen, X. and Brown, C.",
  journal = "Pattern Recognition",
  year = "2004",
  volume = "37",
  number = "9",
  pages = "1757--1771",
}

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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(100 more attributes)

Beach SCUMBLE 0.0005 Sunset SCUMBLE 0 FallFoliage SCUMBLE 0.0004 Field SCUMBLE 0.001 Mountain SCUMBLE 0.0015 Urban SCUMBLE 0

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