Abstract
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 stateof-the-art methods, showing excellent performance.
| Original language | English |
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| Title of host publication | Proceedings of the International Conference on Neural Information Processing (ICONIP 2010) |
| Editors | Conference Program Committee |
| Place of Publication | Berlin, Heidelberg |
| Publisher | Springer |
| Pages | 11 |
| Edition | Peer Reviewed |
| ISBN (Print) | 9783642175336 |
| Publication status | Published - 2010 |
| Event | International Conference on Neural Information Processing (ICONIP 2010) - Sydney Australia, Australia Duration: 1 Jan 2010 → … |
Conference
| Conference | International Conference on Neural Information Processing (ICONIP 2010) |
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| Country/Territory | Australia |
| Period | 1/01/10 → … |
| Other | November 22-25 2010 |