yahoo_arts

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

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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	7484
Attributes	23172
Inputs	23146
Labels	26
Labelsets	599
Single labelsets	358
Max frequency	1240
Cardinality	1.6539
Density	0.0636
Mean IR	94.7379
SCUMBLE	0.0595
TCS	19.7029

Citation

Ueda, N.; Saito, K. (2002). Parametric mixture models for multi-labeled text. In Advances in neural information processing systems, 721--728.

@inproceedings{,
  title="Parametric mixture models for multi-labeled text",
author="Ueda, N. and Saito, K.",
booktitle="Advances in neural information processing systems",
pages="721--728",
year="2002"
}

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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(22972 more attributes)

Label1 SCUMBLE 0.0608 Label2 SCUMBLE 0.4172 Label3 SCUMBLE 0.7006 Label4 SCUMBLE 0.2234 Label5 SCUMBLE 0.0841 Label6 SCUMBLE 0.0685 Label7 SCUMBLE 0.1242 Label8 SCUMBLE 0.0916 Label9 SCUMBLE 0.5364 Label10 SCUMBLE 0.8561 Label11 SCUMBLE 0.09 Label12 SCUMBLE 0.1227 Label13 SCUMBLE 0.0939 Label14 SCUMBLE 0.2142 Label15 SCUMBLE 0.1987 Label16 SCUMBLE 0.0989 Label17 SCUMBLE 0.3712 Label18 SCUMBLE 0.8345 Label19 SCUMBLE 0.6323 Label20 SCUMBLE 0.454 Label21 SCUMBLE 0.0753 Label22 SCUMBLE 0.2873 Label23 SCUMBLE 0.0625 Label24 SCUMBLE 0.061 Label25 SCUMBLE 0.1233 Label26 SCUMBLE 0.0471

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