G. Tsoumakas, E. Loza Mencia, I. Katakis, S. Park, J. Furnkrnaz, “On the combination of two decompositive multi-label classification methods”, Workshop on Preference Learning, ECML PKDD 09, Eyke Hullermeir, Johannes Furnkranz (Ed.), pp. 114-133, Bled, Slovenia, 2009.
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.
