Weighted Complete Graphs for Condensing Data
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Otros documentos de la autoría: Guzmán-Ponce, Angélica; Marcial-Romero, J. Raymundo; Valdovinos Rosas, Rosa María; Sánchez Garreta, Josep Salvador
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Título
Weighted Complete Graphs for Condensing DataAutoría
Fecha de publicación
2020-12-01Editor
Elsevier B.V.ISSN
1571-0661Cita bibliográfica
Guzmán-Ponce, A., Marcial-Romero, J. R., Valdovinos-Rosas, R. M., & Sánchez-Garreta, J. S. (2020). Weighted Complete Graphs for Condensing Data. Electronic Notes in Theoretical Computer Science, 354, 45-60.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In many real-world problems (such as industrial applications, chemistry models, social network analysis,
among others), their solution can be obtained by transforming the problem in terms of vertices and edges,
that ... [+]
In many real-world problems (such as industrial applications, chemistry models, social network analysis,
among others), their solution can be obtained by transforming the problem in terms of vertices and edges,
that is to say, using graph theory. Data Science applications are characterized by processing large volumes
of data, in some cases, the data size can be higher than the resources for their processing, situation that
makes prohibitive to use the traditional methods. In this way, to develop solutions based on graphs for
condensing data can be a good strategy for handling big datasets. In this paper we include two methods for
condensing data based on graphs, the two proposals consider a weighted complete graph by acquiring an
induced subgraph or a minimum spanning tree from the whole datasets. We conducted some experiments
in order to validate our proposals, using 24 benchmark real-datasets for training the 1NN, C4.5, and SVM
classifiers. The results prove that our methods condensed the datasets without reducing the performance
of the classifier, in terms of geometric means and the Wilcoxon’s test. [-]
Publicado en
Electronic Notes in Theoretical Computer Science, Vol. 354 (december 2020)Entidad financiadora
Universitat Jaume I | Mexican CONACYT
Código del proyecto o subvención
UJI-B2018-49 | 5046/2020CIC
Título del proyecto o subvención
UAEM proyect
Derechos de acceso
info:eu-repo/semantics/openAccess
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