Tsoumakas, G.; Katakis, I.; Vlahavas, I. (2008). Effective and Efficient Multilabel Classification in Domains with Large Number of Labels. In Proc. ECML/PKDD Workshop on Mining Multidimensional Data, Antwerp, Belgium, MMD08, 30--44.
delicious
mldr.datasets::get.mldr("delicious")
Summary
Instances | 16105 |
---|---|
Attributes | 1483 |
Inputs | 500 |
Labels | 983 |
Labelsets | 15806 |
Single labelsets | 15642 |
Max frequency | 19 |
Cardinality | 19.02 |
Density | 0.0193 |
Mean IR | 71.1338 |
SCUMBLE | 0.532 |
TCS | 22.7734 |
Citation
Tsoumakas, G.; Katakis, I.; Vlahavas, I. (2008). Effective and Efficient Multilabel Classification in Domains with Large Number of Labels. In Proc. ECML/PKDD Workshop on Mining Multidimensional Data, Antwerp, Belgium, MMD08, 30--44.
@inproceedings{,
author = "Tsoumakas, G. and Katakis, I. and Vlahavas, I.",
title = "Effective and Efficient Multilabel Classification in Domains with Large Number of Labels",
booktitle = "Proc. ECML/PKDD Workshop on Mining Multidimensional Data, Antwerp, Belgium, MMD08",
pages = "30--44",
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.