Label Ranking Datasets
Semi-synthetic
| Dataset | type | # Instances | # Features | # Labels |
| authorship | A | 841 | 70 | 4 |
| bodyfat | B | 252 | 7 | 7 |
| calhousing | B | 20640 | 4 | 4 |
| cpu-small | B | 8192 | 6 | 5 |
| elevators | B | 16599 | 9 | 9 |
| fried | B | 40769 | 9 | 5 |
| glass | A | 214 | 9 | 6 |
| housing | B | 506 | 6 | 6 |
| iris | A | 150 | 4 | 3 |
| pendigits | A | 10992 | 16 | 10 |
| segment | A | 2310 | 18 | 7 |
| stock | B | 950 | 5 | 5 |
| vehicle | A | 846 | 18 | 4 |
| vowel | A | 528 | 10 | 11 |
| wine | A | 178 | 13 | 3 |
| wisconsin | B | 194 | 16 | 16 |
Download (in .rar-format)
These datasets have been used in:
- Weiwei Cheng, Jens Hühn, Eyke Hüllermeier
Decision tree and instance-based learning for label ranking [pdf][bibtex][slides][poster][video]
Proceedings of the 26th International Conference on Machine Learning (ICML-09): 161-168, Omnipress
Montreal, Canada, June 2009
- Weiwei Cheng, Krzysztof Dembczyński, Eyke Hüllermeier
Label ranking methods based on the Plackett-Luce model [pdf][bibtex][slides][poster]
Proceedings of the 27th International Conference on Machine Learning (ICML-10): 215-222, Omnipress
Haifa, Israel, June 2010
Real-world
| Dataset | # Instances | # Features | # Labels |
| spo | 2465 | 24 | 11 |
| heat | 2465 | 24 | 6 |
| dtt | 2465 | 24 | 4 |
| cold | 2465 | 24 | 4 |
| diau | 2465 | 24 | 7 |
Download (in .rar-format)
These datasets have been used in:
- Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, Klaus Brinker
Label ranking by learning pairwise preferences [pdf] [bibtex]
Artificial Intelligence 172: 1897-1916, Elsevier