| Title: |
On the combination of two decompositive multi-label classification methods |
| Author(s): |
G. Tsoumakas, E. Loza Mencia, I. Katakis, S. Park, J. Furnkrnaz.
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| Availability: |
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| Keywords: |
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| Appeared in: |
Workshop on Preference Learning, ECML PKDD 09, Eyke Hullermeir, Johannes Furnkranz (Ed.), pp. 114-133, Bled, Slovenia, 2009.
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| Abstract: |
In this paper, we compare and combine two approaches for multi-label classification 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 classifiers. 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 beneficial for the pairwise preference-based approach in terms of computational cost and quality of prediction. |
| See also : |
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This paper has been cited by the following:
| 1 |
Wang, H., Shi, Y., Zhouy, X., Zhou, Q., Shao, S., Bouguettaya, A. (2010) Web Service Classification using Support Vector Machine, Proc. 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010, Arras, France, 27-29 October 2010 - Volume 1. IEEE Computer Society 2010, ISBN 978-0-7695-4263-8 |
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