rcv1sub4

mldr.datasets::get.mldr("rcv1sub4")

Select your download

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 6000
Attributes 47330
Inputs 47229
Labels 101
Labelsets 816
Single labelsets 491
Max frequency 950
Cardinality 2.4837
Density 0.0246
Mean IR 89.3713
SCUMBLE 0.2165
TCS 22.0823

Citation

Lewis, D. D.; Yang, Y.; Rose, T. G.; Li, F. (2004). RCV1: A new benchmark collection for text categorization research. In The Journal of Machine Learning Research, 5(), 361--397.
@article{,
  title="RCV1: A new benchmark collection for text categorization research",
  author="Lewis, D. D. and Yang, Y. and Rose, T. G. and Li, F.",
  journal="The Journal of Machine Learning Research",
  volume="5",
  pages="361--397",
  year="2004"
}

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.