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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

 

Further information:

The University for the Information Society