tmc2007

mldr.datasets::get.mldr("tmc2007")

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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 28596
Attributes 49082
Inputs 49060
Labels 22
Labelsets 1341
Single labelsets 662
Max frequency 2486
Cardinality 2.1579
Density 0.0981
Mean IR 15.1567
SCUMBLE 0.1747
TCS 21.093

Citation

Srivastava, A. N.; Zane-Ulman, B. (2005). Discovering recurring anomalies in text reports regarding complex space systems. In Aerospace Conference, 3853--3862.
@inproceedings{,
  title="Discovering recurring anomalies in text reports regarding complex space systems",
  author="Srivastava, A. N. and Zane-Ulman, B.",
  booktitle="Aerospace Conference",
  pages="3853--3862",
  year="2005",
}

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.