PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Reverse Multi-Label Learning
James Petterson and Tiberio Caetano
In: Advances in Neural Information Processing Systems (NIPS)(2011).


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:7333
Deposited By:Wray Buntine
Deposited On:17 March 2011