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dc.contributor.authorWicker, Jörg
dc.contributor.authorPfahringer, Bernhard
dc.contributor.authorKramer, Stefan
dc.coverage.spatialConference held at Trento, Italyen_NZ
dc.identifier.citationWicker, J., Pfahringer, B. & Kramer, S. (2012). Multi-label classification using boolean matrix decomposition. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC '12). ACM, New York, 179-186.en_NZ
dc.description.abstractThis paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.en_NZ
dc.relation.ispartofProceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12
dc.subjectMachine learning
dc.titleMulti-label classification using boolean matrix decompositionen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProceedings of the 27th Annual ACM Symposium on Applied Computingen_NZ
pubs.finish-date2012-03-30en_NZ York, NYen_NZ

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