Exploring early classification strategies of streaming data with delayed attributes
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Millán Giraldo, Mónica; Sánchez Garreta, Josep Salvador; Traver Roig, Vicente Javier
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Exploring early classification strategies of streaming data with delayed attributesFecha de publicación
2009Editor
Springer Verlag (Germany)ISSN
0302-9743Cita bibliográfica
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.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.
Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://link.springer.com/chapter/10.1007/978-3-642-10677-4_99https://link.springer.com/chapter/10.1007/978-3-642-10677-4_99
Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
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 ... [+]
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 [-]
Publicado en
Lecture notes in computer science, 2009, v. 5863Derechos de acceso
© Springer Verlag (Germany)
http://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
info:eu-repo/semantics/openAccess
http://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- LSI_Articles [360]