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On the Problem of Error Propagation in classifier chains for multi-label classification
dc.contributor.author | Senge, Robin | |
dc.contributor.author | Coz Velasco, Juan José del | |
dc.contributor.author | Hüllermeier, Eyke | |
dc.date.accessioned | 2016-03-18T09:20:46Z | |
dc.date.available | 2016-03-18T09:20:46Z | |
dc.date.issued | 2013 | |
dc.identifier.isbn | 978-3-319-01594-1 | |
dc.identifier.isbn | 978-3-319-01595-8 | |
dc.identifier.uri | http://hdl.handle.net/10651/35746 | |
dc.description.abstract | So-called classifier chains have recently been proposed as an appealing method for tackling the multi-label classification task. In this paper, we analyze the influence of a potential pitfall of the learning process, namely the discrepancy between the feature spaces used in training and testing: While true class labels are used as supplementary attributes for training the binary models along the chain, the same models need to rely on estimations of these labels when making a prediction. We provide first experimental results suggesting that the attribute noise thus created can affect the overall prediction performance of a classifier chain | spa |
dc.description.sponsorship | This research has been supported by the Germany Research Foundation (DFG) and the Spanish Ministerio de Ciencia e Innovación (MICINN) under grant TIN2011-23558 | spa |
dc.format.extent | p. 163-170 | spa |
dc.language.iso | eng | spa |
dc.publisher | Springer | spa |
dc.relation.ispartof | Data Analysis, Machine Learning and Knowledge Discovery | spa |
dc.rights | © 2013 Springer | |
dc.title | On the Problem of Error Propagation in classifier chains for multi-label classification | spa |
dc.type | book part | spa |
dc.identifier.doi | 10.1007/978-3-319-01595-8_18 | |
dc.relation.projectID | MEC/TIN2011-23558 | spa |
dc.relation.publisherversion | http://dx.doi.org/10.1007/978-3-319-01595-8_18 | spa |
dc.rights.accessRights | open access | |
dc.type.hasVersion | AM |
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