tmc2007_500

mldr.datasets::get.mldr("tmc2007_500")

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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 522
Inputs 500
Labels 22
Labelsets 1172
Single labelsets 408
Max frequency 2484
Cardinality 2.2196
Density 0.1009
Mean IR 17.1343
SCUMBLE 0.1927
TCS 16.3721

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.