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.
eurlexsm
mldr.datasets::get.mldr("eurlexsm")
Summary
Instances | 19348 |
---|---|
Attributes | 5201 |
Inputs | 5000 |
Labels | 201 |
Labelsets | 2504 |
Single labelsets | 1182 |
Max frequency | 1041 |
Cardinality | 2.2133 |
Density | 0.011 |
Mean IR | 536.9761 |
SCUMBLE | 0.182 |
TCS | 21.6461 |
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.