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.
eurlexev
mldr.datasets::get.mldr("eurlexev")
Summary
Instances | 19348 |
---|---|
Attributes | 8993 |
Inputs | 5000 |
Labels | 3993 |
Labelsets | 16467 |
Single labelsets | 14609 |
Max frequency | 34 |
Cardinality | 5.3102 |
Density | 0.0013 |
Mean IR | 396.636 |
SCUMBLE | 0.4201 |
TCS | 26.5186 |
Citation
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.
@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.