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On the Combination of Two Decompositive Multi-Label Classification Methods
Type of publication: Inproceedings
Citation: jf:PL-09-WS-Paper
Booktitle: Proceedings of the ECML PKDD 2009 Workshop on Preference Learning (PL-09, Bled, Slovenia)
Year: 2009
Pages: 114--129
URL: http://www.ke.tu-darmstadt.de/events/PL-09/09-Tsoumakas.pdf
Abstract: 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.
Authors Tsoumakas, Grigorios
Loza Mencía, Eneldo
Katakis, Ioannis
Park, Sang-Hyeun
Fürnkranz, Johannes
Editors Hüllermeier, Eyke
Fürnkranz, Johannes
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