Mostrar o rexistro simple do ítem
A One-Class Classification method based on Expanded Non-Convex Hulls
dc.contributor.author | Novoa-Paradela, David | |
dc.contributor.author | Fontenla-Romero, Óscar | |
dc.contributor.author | Guijarro-Berdiñas, Bertha | |
dc.date.accessioned | 2022-10-14T14:16:36Z | |
dc.date.available | 2022-10-14T14:16:36Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | D. Novoa-Paradela, O. Fontenla-Romero, y B. Guijarro-Berdiñas, «A One-Class Classification method based on Expanded Non-Convex Hulls», Information Fusion, vol. 89, pp. 1-15, ene. 2023, doi: 10.1016/j.inffus.2022.07.023. | es_ES |
dc.identifier.issn | 1566-2535 | |
dc.identifier.uri | http://hdl.handle.net/2183/31809 | |
dc.description.abstract | [Abstract]: This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation on the construction of subdivisible and expandable non-convex hulls to represent the target class. The method begins by reducing the dimensionality of the data to two-dimensional spaces using random projections. After that, an iterative process based on Delaunay triangulations is applied to these spaces to obtain simple polygons that characterizes the non-convex shape of the normal class data. In addition, the method subdivides the non-convex hulls to represent separate regions in space if necessary. The method has been evaluated and compared to several main algorithms of the field using real data sets. In contrast to other methods, OCENCH can deal with non-convex and disjointed shapes. Finally, its execution can be carried out in a parallel way, which is interesting to reduce the execution time. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | Agencia Estatal de Investigación; PID2019-109238GB-C2 | es_ES |
dc.relation | Xunta de Galicia; ED431C 2018/34 | es_ES |
dc.relation | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.inffus.2022.07.023 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Machine learning | es_ES |
dc.subject | One-Class Classification | es_ES |
dc.subject | Convex Hull | es_ES |
dc.subject | Delaunay triangulation | es_ES |
dc.subject | Random projections | es_ES |
dc.subject | Ensemble learning | es_ES |
dc.title | A One-Class Classification method based on Expanded Non-Convex Hulls | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Information Fusion | es_ES |
UDC.volume | 89 | es_ES |
UDC.issue | January | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 15 | es_ES |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-LIDIA - Artigos [63]