Semi-supervised Time Series Classification Through Image Representations

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRozin, Bionda-
Autor(es): dc.creatorBergamim, Emílio-
Autor(es): dc.creatorPedronette, Daniel Carlos Guimarães-
Autor(es): dc.creatorBreve, Fabricio Aparecido-
Data de aceite: dc.date.accessioned2025-08-21T22:30:22Z-
Data de disponibilização: dc.date.available2025-08-21T22:30:22Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-36808-0_4-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307452-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307452-
Descrição: dc.descriptionTime series data is of crucial importance in different domains, such as financial and medical applications. However, obtaining a large amount of labeled time series data is an expensive and time-consuming task, which becomes the process of building an effective machine learning model a challenge. In these scenarios, algorithms that can deal with reduced amounts of labeled data emerge. One example is Semi-Supervised Learning (SSL), which has the capability of exploring both labeled and unlabeled data for tasks such as classification. In this work, a kNN graph-based transductive SSL approach is used for time series classification. A feature extraction step, based on imaging time series and obtaining features using deep neural networks is performed before the classification step. An extensive evaluation is conducted over four datasets, and a parametric analysis of the nearest neighbors is performed. Also, a statistical analysis over the obtained distances is conducted. Results suggest that our methods are suitable for classification and competitive with supervised baselines in some datasets.-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC). Sao Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Statistics Applied Mathematics and Computing (DEMAC). Sao Paulo State University (UNESP)-
Formato: dc.format48-65-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectClassification-
Palavras-chave: dc.subjectFeature Extraction-
Palavras-chave: dc.subjectGraph-
Palavras-chave: dc.subjectImages-
Palavras-chave: dc.subjectNeural Networks-
Palavras-chave: dc.subjectTime Series-
Palavras-chave: dc.subjectTransductive Semi Supervised Learning-
Título: dc.titleSemi-supervised Time Series Classification Through Image Representations-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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