Mencia, E. L.; F{ (2008). Efficient pairwise multilabel classification for large-scale problems in the legal domain. In Machine Learning and Knowledge Discovery in Databases, 50--65.
eurlexdc
mldr.datasets::get.mldr("eurlexdc")
Summary
| Instances | 19348 |
|---|---|
| Attributes | 5412 |
| Inputs | 5000 |
| Labels | 412 |
| Labelsets | 1615 |
| Single labelsets | 717 |
| Max frequency | 1633 |
| Cardinality | 1.2923 |
| Density | 0.0031 |
| Mean IR | 268.9297 |
| SCUMBLE | 0.048 |
| TCS | 21.9253 |
Citation
@incollection{,
title="Efficient pairwise multilabel classification for large-scale problems in the legal domain",
author="Mencia, E. L. and F{"u}rnkranz, J.",
booktitle="Machine Learning and Knowledge Discovery in Databases",
pages="50--65",
year="2008"
}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.