TY  - CONF
ID  - jf:PL-09-WS-Paper
T1  - On the Combination of Two Decompositive Multi-Label Classification Methods
A1  - Tsoumakas, Grigorios
A1  - Loza Mencía, Eneldo
A1  - Katakis, Ioannis
A1  - Park, Sang-Hyeun
A1  - Fürnkranz, Johannes
ED  - Hüllermeier, Eyke
ED  - Fürnkranz, Johannes
TI  - Proceedings of the ECML PKDD 2009 Workshop on Preference Learning (PL-09, Bled, Slovenia)
Y1  - 2009
SP  - 114
EP  - 129
UR  - /events/PL-09/09-Tsoumakas.pdf
N2  - In this paper, we compare and combine two approaches for
multi-label classication that both decompose the initial problem into
sets of smaller problems. The Calibrated Label Ranking approach is
based on interpreting the multi-label problem as a preference learning
problem and decomposes it into a quadratic number of binary classiers.
The HOMER approach reduces the original problem into a hierarchy of
considerably simpler multi-label problems. Experimental results indicate
that the use of HOMER is benecial for the pairwise preference-based
approach in terms of computational cost and quality of prediction.
ER  -