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Exploring early classification strategies of streaming data with delayed attributes
dc.contributor.author | Millán Giraldo, Mónica | |
dc.contributor.author | Sánchez Garreta, Josep Salvador | |
dc.contributor.author | Traver Roig, Vicente Javier | |
dc.date.accessioned | 2011-06-16T07:19:16Z | |
dc.date.available | 2011-06-16T07:19:16Z | |
dc.date.issued | 2009 | |
dc.identifier.citation | MILLÁN-GIRALDO, Mónica; SÁNCHEZ, J. Salvador; TRAVER, V. Javier. Exploring early classification strategies of streaming data with delayed attributes. En International Conference on Neural Information Processing. Springer, Berlin, Heidelberg, 2009. p. 875-883. | |
dc.identifier.citation | MILLÁN-GIRALDO, Mónica; SÁNCHEZ, J. Salvador; TRAVER, V. Javier. Exploring early classification strategies of streaming data with delayed attributes. En International Conference on Neural Information Processing. Springer, Berlin, Heidelberg, 2009. p. 875-883. | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/10234/23621 | |
dc.description.abstract | In contrast to traditional machine learning algorithms, where all data are available in batch mode, the new paradigm of streaming data poses additional difficulties, since data samples arrive in a sequence and many hard decisions have to be made on-line. The problem addressed here consists of classifying streaming data which not only are unlabeled, but also have a number l of attributes arriving after some time delay. In this context, the main issues are what to do when the unlabeled incomplete samples and, later on, their missing attributes arrive; when and how to classify these incoming samples; and when and how to update the training set. Three different strategies (for l = 1 and constant) are explored and evaluated in terms of the accumulated classification error. The results reveal that the proposed on-line strategies, despite their simplicity, may outperform classifiers using only the original, labeled-and-complete samples as a fixed training set. In other words, learning is possible by properly tapping into the unlabeled, incomplete samples, and their delayed attributes. The many research issues identified include a better understanding of the link between the inherent properties of the data set and the design of the most suitable on-line classification strategy | |
dc.format.extent | 10 p. | |
dc.language.iso | eng | |
dc.publisher | Springer Verlag (Germany) | |
dc.relation.isFormatOf | Versió pre-print del document publicat a: http://www.springerlink.com/content/61l730286324j336/ | |
dc.relation.isPartOf | Lecture notes in computer science, 2009, v. 5863 | |
dc.rights | © Springer Verlag (Germany) | |
dc.rights.uri | http://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0 | |
dc.subject | Data mining | |
dc.subject | Streaming data | |
dc.subject | On-line classification | |
dc.subject | Missing attributes | |
dc.subject.lcsh | Data mining | |
dc.subject.other | Mineria de dades | |
dc.title | Exploring early classification strategies of streaming data with delayed attributes | |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | http://dx.doi.org/10.1007/978-3-642-10677-4_99 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | |
dc.relation.publisherVersion | https://link.springer.com/chapter/10.1007/978-3-642-10677-4_99 | |
dc.relation.publisherVersion | https://link.springer.com/chapter/10.1007/978-3-642-10677-4_99 | |
dc.type.version | info:eu-repo/semantics/acceptedVersion |
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