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Multi-label classification using boolean matrix decomposition

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dc.contributor.author Wicker, Jörg
dc.contributor.author Pfahringer, Bernhard
dc.contributor.author Kramer, Stefan
dc.date.accessioned 2012-09-17T03:01:04Z
dc.date.available 2012-09-17T03:01:04Z
dc.date.copyright 2012
dc.date.issued 2012
dc.identifier.citation Wicker, 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.identifier.uri http://hdl.handle.net/10289/6632
dc.description.abstract This 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.language.iso en
dc.publisher ACM en_NZ
dc.relation.ispartof Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12
dc.title Multi-label classification using boolean matrix decomposition en_NZ
dc.type Conference Contribution en_NZ
dc.identifier.doi 10.1145/2245276.2245311 en_NZ


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