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Genetic programming and serial processing for time series classification

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Genetic programming and serial processing for time series classification

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dc.contributor.author Alfaro Cid, Eva es_ES
dc.contributor.author Sharman, Kenneth Charles es_ES
dc.contributor.author Esparcia Alcázar, Anna Isabel es_ES
dc.date.accessioned 2016-07-13T10:33:57Z
dc.date.available 2016-07-13T10:33:57Z
dc.date.issued 2014-06
dc.identifier.issn 1063-6560
dc.identifier.uri http://hdl.handle.net/10251/67540
dc.description.abstract This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets. es_ES
dc.language Inglés es_ES
dc.publisher Massachusetts Institute of Technology Press (MIT Press) es_ES
dc.relation.ispartof Evolutionary Computation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Time series es_ES
dc.subject Real world applications es_ES
dc.subject Serial data processing es_ES
dc.subject Classification es_ES
dc.subject Genetic programming es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Genetic programming and serial processing for time series classification es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1162/EVCO_a_00110
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto Tecnológico de Informática - Institut Universitari Mixt Tecnològic d'Informàtica es_ES
dc.description.bibliographicCitation Alfaro Cid, E.; Sharman, KC.; Esparcia Alcázar, AI. (2014). Genetic programming and serial processing for time series classification. Evolutionary Computation. 22(2):265-285. doi:10.1162/EVCO_a_00110 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi.org/10.1162/EVCO_a_00110 es_ES
dc.description.upvformatpinicio 265 es_ES
dc.description.upvformatpfin 285 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 2 es_ES
dc.relation.senia 290184 es_ES


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