%Aigaion2 BibTeX export from Knowledge Engineering Publications %Friday 17 December 2021 11:58:21 PM @INPROCEEDINGS{jf:PL-09-WS-Paper, author = {Tsoumakas, Grigorios and Loza Menc{\'{\i}}a, Eneldo and Katakis, Ioannis and Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes}, editor = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes}, title = {On the Combination of Two Decompositive Multi-Label Classification Methods}, booktitle = {Proceedings of the ECML PKDD 2009 Workshop on Preference Learning (PL-09, Bled, Slovenia)}, year = {2009}, pages = {114--129}, url = {/events/PL-09/09-Tsoumakas.pdf}, abstract = {In this paper, we compare and combine two approaches for multi-label classication 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 classiers. 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 benecial for the pairwise preference-based approach in terms of computational cost and quality of prediction.} }