On the Combination of Two Decompositive Multi-Label Classification Methods
Grigorios Tsoumakas, Eneldo Loza Mencía, Ioannis Katakis, Sang-Hyeun Park, Johannes Fürnkranz · 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.