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TINTO: Converting Tidy Data into image for classification with 2-Dimensional Convolutional Neural Networks

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dc.contributor.authorTalla Chumpitaz, Reewos
dc.contributor.authorGarcía Castro, Raúl
dc.contributor.authorOrozco Barbosa, Luis
dc.contributor.authorCastillo Cara, José Manuel
dc.date.accessioned2024-05-20T11:36:50Z
dc.date.available2024-05-20T11:36:50Z
dc.date.issued2023
dc.description.abstractThe growing interest in the use of algorithms-based machine learning for predictive tasks has generated a large and diverse development of algorithms. However, it is widely known that not all of these algorithms are adapted to efficient solutions in certain tidy data format datasets. For this reason, novel techniques are currently being developed to convert tidy data into images with the aim of using Convolutional Neural Networks (CNNs). TINTO offers the opportunity to convert tidy data into images through the representation of characteristic pixels by implementing two dimensional reduction algorithms: Principal Component Analysis (PCA) and t-distributed Stochastic Neighbour Embedding (t-SNE). Our proposal also includes a blurring technique, which adds more ordered information to the image and can improve the classification task in CNNs.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1016/j.softx.2023.101391
dc.identifier.issn2352-7110
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12308
dc.journal.titleSoftwareX
dc.journal.volume22
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInformática y Automática
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject.keywordsTabular to image conversion
dc.subject.keywordsImage classification
dc.subject.keywordsImage blurring technique
dc.subject.keywordsImage generation
dc.subject.keywordsConvolutional neural networks
dc.subject.keywordsTabular data into image
dc.titleTINTO: Converting Tidy Data into image for classification with 2-Dimensional Convolutional Neural Networkses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationc0e39bd2-c0d8-4743-953d-488baf6b977e
relation.isAuthorOfPublication.latestForDiscoveryc0e39bd2-c0d8-4743-953d-488baf6b977e
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