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: | /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. |
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