http://mulan.sourceforge.net/datasets-mlc.html
name |
domain |
instances |
nominal |
numeric |
labels |
cardinality |
density |
distinct |
yeast |
biology |
2417 |
0 |
103 |
14 |
4.237 |
0.303 |
198 |
evaluation criterion |
BR |
CC |
ECC |
PCC(效果很差) |
hamming loss |
0.2268266085059978 |
0.2268266085059978 |
0.23298021498675806 |
0.5221218258295685 |
ranking loss |
0.16849462724050177 |
0.16860606695590194 |
0.045019261908574866 |
|
one error |
0.24532453245324531 |
0.25192519251925194 |
0.24972737186477645 |
|
- Python 3.6
- numpy 1.13.3
- scikit-learn 0.19.1
- ECC algorithm chain number:10
- ECC algorithm subset proportion:0.75
Jesse Read·Bernhard Pfahringer·Geoff Holmes·Eibe Frank, “Classifier chains for multi-label classification,” Machine Learning, vol. 85, no. 3, pp. 333–359, 2011
K. Dembczy´nski, W. Cheng, and E. H¨ullermeier, “Bayes optimal multilabel classification via probabilistic classifier chains,” in Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel, 2010, pp. 279–286