dc.creator | González Abril, Luis | es |
dc.creator | Velasco Morente, Francisco | es |
dc.creator | Ortega Ramírez, Juan Antonio | es |
dc.creator | Cuberos, Francisco Javier | es |
dc.date.accessioned | 2023-02-14T07:57:47Z | |
dc.date.available | 2023-02-14T07:57:47Z | |
dc.date.issued | 2009-08 | |
dc.identifier.citation | González Abril, L., Velasco Morente, F., Ortega Ramírez, J.A. y Cuberos, F.J. (2009). A new approach to qualitative learning in time series. Expert Systems with Applications, 36 (6), 9924-9927. https://doi.org/10.1016/j.eswa.2009.01.066. | |
dc.identifier.issn | 0957-4174 (impreso) | es |
dc.identifier.issn | 1873-6793 (online) | es |
dc.identifier.uri | https://hdl.handle.net/11441/142689 | |
dc.description.abstract | In this paper the k-nearest-neighbours (KNN) based method is presented for the classification of time series which use qualitative learning to identify similarities using kernels. To this end, time series are transformed into symbol strings by means of several discretization methods and a distance based on a kernel between symbols in ordinal scale is used to calculate the similarity between time series. Hence, the idea proposed is the consideration of the simultaneous use of symbolic representation together with a kernel based approach for classification of time series. The methodology has been tested and compared with quantitative learning from a television-viewing shared data set and has yielded a high success identification ratio. | es |
dc.description.sponsorship | Ministerio de Educación y Ciencia TSI2006-13390-C02-02 | es |
dc.description.sponsorship | Junta de Andalucía P06-TIC-02141 | es |
dc.format | application/pdf | es |
dc.format.extent | 4 | es |
dc.language.iso | eng | es |
dc.publisher | ScienceDirect | es |
dc.relation.ispartof | Expert Systems with Applications, 36 (6), 9924-9927. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Discretization | es |
dc.subject | k-nearest-neighbours | es |
dc.subject | Kernel | es |
dc.subject | Similarity | es |
dc.title | A new approach to qualitative learning in time series | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Economía Aplicada I | |
dc.relation.projectID | TSI2006-13390-C02-02 | es |
dc.relation.projectID | P06-TIC-02141 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0957417409001122 | es |
dc.identifier.doi | 10.1016/j.eswa.2009.01.066 | es |
dc.journaltitle | Expert Systems with Applications | es |
dc.publication.volumen | 36 | es |
dc.publication.issue | 6 | es |
dc.publication.initialPage | 9924 | es |
dc.publication.endPage | 9927 | es |
dc.contributor.funder | Ministerio de Educación y Ciencia (MEC). España | es |
dc.contributor.funder | Junta de Andalucía | es |