Mostrar o rexistro simple do ítem
Improving detection of apneic events by learning from examples and treatment of missing data
dc.contributor.author | Hernández-Pereira, Elena | |
dc.contributor.author | Álvarez-Estévez, Diego | |
dc.contributor.author | Moret-Bonillo, Vicente | |
dc.date.accessioned | 2017-02-16T20:05:42Z | |
dc.date.available | 2017-02-16T20:05:42Z | |
dc.date.issued | 2014 | |
dc.identifier.citation | Elena Hernández-Pereira, Diego Álvarez-Estévez, Vicente Moret-Bonillo. Improving detection of apneic events by learning from examples and treatment of missing data. Studies in Health Technology and Informatics 207 (2014), 213 - 224. | es_ES |
dc.identifier.issn | 0926-9630 | |
dc.identifier.issn | 1879-8365 | |
dc.identifier.uri | http://hdl.handle.net/2183/18135 | |
dc.description | The final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-474-9-213 | es_ES |
dc.description.abstract | [Abstract] This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best performance regardless the imputation method used. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | I O S Press | es_ES |
dc.relation.uri | http://ebooks.iospress.nl/volumearticle/38639 | es_ES |
dc.subject | Respiratory pattern classification | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Algorithms | es_ES |
dc.subject | Feedforward neural network | es_ES |
dc.title | Improving detection of apneic events by learning from examples and treatment of missing data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Studies in Health Technology and Informatics | es_ES |
UDC.volume | 207 | es_ES |
UDC.startPage | 213 | es_ES |
UDC.endPage | 224 | es_ES |
dc.identifier.doi | 10.3233/978-1-61499-474-9-213 |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-LIDIA - Artigos [64]