Mulan: A Java Library for Multi-Label Learning

Datasets

Mulan was recently extended for multi-target regression (MTR). Below you can find a list of benchmark MTR datasets that we have collected along with the corresponding sources and citations. For more details on each dataset see the corresponding paper(s). All datasets can be downloaded from here!

Statistics

name	examples	features	targets	Source/Citation
atp1d	337	411	6	[1]
atp7d	296	411	6	[1]
oes97	334	263	16	[1]
oes10	403	298	16	[1]
rf1	9125	64	8	[1]
rf2	9125	576	8	[1]
scm1d	9803	280	16	[1]
scm20d	8966	61	16	[1]
edm	154	16	2	[2]
sf1	323	10	3	[3]
sf2	1066	10	3	[3]
jura	359	15	3	[4],[1]
wq	1060	16	14	[5]
enb	768	8	2	[6], [1]
slump	103	7	3	[7], [1]
andro	49	30	6	[8], [1]
osales	639	413	12	[9], [1]
scfp	1137	23	3	[10], [1]

Sources / Citations

E. Spyromitros-Xioufis, G. Tsoumakas, W. Groves, I. Vlahavas, "Multi-Target Regression via Input Space Expansion: Treating Targets as Inputs", Machine Learning, 2016. [paper] [bibtex]
A. Karalic, I. Bratko, "First Order Regression", Machine Learning, 1997.
UCI repository
Gooaverts P., "Geostatistics for natural resources evaluation", Oxford university press, 1997.
S. Dzeroski, D. Demsar, J. Grbovic, "Predicting Chemical Parameters of River Water Quality from Bioindicator Data", Applied Intelligence, 2000.
A. Tsanas, A. Xifara, "Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools", Energy and Buildings, 2012.
IC. Yeh, "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement and Concrete Composites, 2007.
EV. Hatzikos, "An empirical study on sea water quality prediction", Knowledge-based systems, 2008.
Kaggle competition: Online Product Sales
Kaggle competition: See Click Predict Fix