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dc.contributor.authorIrcio, J.
dc.contributor.authorLojo, A.
dc.contributor.authorMori, U.
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2020-10-20T19:15:13Z
dc.date.available2020-10-20T19:15:13Z
dc.date.issued2020
dc.identifier.isbn978-1-4939-3843-8
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1186
dc.description.abstractThis paper deals with supervised classification of multivariate time se- ries. In particular, the goal is to propose a filter method to select a subset of time series. Consequently, we adopt the framework proposed by Brown et al. [10]. The key point in this framework is the computation of the mutual information between the features, which allows us to measure the relevance of each feature subset. In our case, where the features are a time series, we use an adaptation of existing nonparametric mutual infor- mation estimators based on the k-nearest neighbor. Specifically, for the purpose of bringing these methods to the time series scenario, we rely on the use of dynamic time warping dissimilarity. Our experimental results show that our method is able to strongly reduce the number of time series while keeping or increasing the classification accuracy.en_US
dc.description.sponsorshipGrant agreement no. KK-2019/00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I00en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectMultivariate time seriesen_US
dc.subjectSupervised classificationen_US
dc.subjectFeature susbset selectionen_US
dc.subjectMutual informationen_US
dc.titleMutual information based feature subset selection in multivariate time series classificationen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publisherversionPattern Recognition 108, 107525en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titlePattern Recognitionen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España