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Poster
Reverse Multi-Label Learning
James Petterson · Tiberio Caetano
Multi-label classification is the task of predicting potentially multiple labels for a given instance. This is common in several applications such as image annotation, document classification and gene function prediction. In this paper we present a formulation for this problem based on reverse prediction: we predict sets of instances given the labels. By viewing the problem from this perspective, the most popular quality measures for assessing the performance of multi-label classification admit relaxations that can be efficiently optimised. We optimise these relaxations with standard algorithms and compare our results with several state-of-the-art methods, showing excellent performance.
Author Information
James Petterson (NICTA)
Tiberio Caetano (NICTA Canberra)
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