yahoo_society

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

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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	14512
Attributes	31829
Inputs	31802
Labels	27
Labelsets	1054
Single labelsets	624
Max frequency	4092
Cardinality	1.6704
Density	0.0619
Mean IR	302.0678
SCUMBLE	0.0957
TCS	20.6235

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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(31629 more attributes)

Label1 SCUMBLE 0.1297 Label2 SCUMBLE 0.1461 Label3 SCUMBLE 0.1645 Label4 SCUMBLE 0.1463 Label5 SCUMBLE 0.1057 Label6 SCUMBLE 0.1094 Label7 SCUMBLE 0.1586 Label8 SCUMBLE 0.4259 Label9 SCUMBLE 0.9865 Label10 SCUMBLE 0.1266 Label11 SCUMBLE 0.4318 Label12 SCUMBLE 0.184 Label13 SCUMBLE 0.2668 Label14 SCUMBLE 0.6967 Label15 SCUMBLE 0.6556 Label16 SCUMBLE 0.0846 Label17 SCUMBLE 0.281 Label18 SCUMBLE 0.1526 Label19 SCUMBLE 0.0993 Label20 SCUMBLE 0.1481 Label21 SCUMBLE 0.2176 Label22 SCUMBLE 0.1871 Label23 SCUMBLE 0.3861 Label24 SCUMBLE 0.5729 Label25 SCUMBLE 0.2157 Label26 SCUMBLE 0.7639 Label27 SCUMBLE 0.1924

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