A Fast Multivariate Symmetrical Uncertainty Based Heuristic for High Dimensional Feature Selection.

dc.contributor.author	García Torres, Miguel
dc.contributor.author	Divina, Federico
dc.contributor.author	Gómez, Francisco
dc.contributor.author	Vázquez Noguera, José Luis
dc.contributor.other	Universidad Americana (PY)	es
dc.contributor.other	Universidad Pablo de Olavide (ES)	es
dc.date.accessioned	2022-04-29T23:02:38Z
dc.date.available	2022-04-29T23:02:38Z
dc.date.issued	2021
dc.identifier.uri	http://hdl.handle.net/20.500.14066/3778
dc.description.abstract	In classification tasks the increase in the number of dimensions of a data makes the learning process harder. In this context feature selection usually allows to induce simpler classifier models while keeping the accuracy. However, some factors, such as the presence of irrelevant and redundant features, make the feature selection process challenging.	es
dc.description.sponsorship	CONACYT - Consejo Nacional de Ciencia y Tecnología	es
dc.language.iso	eng	es
dc.subject.classification	7 Salud	es
dc.subject.other	CANCER	es
dc.subject.other	MELANOMA	es
dc.subject.other	ONCOLOGIA	es
dc.title	A Fast Multivariate Symmetrical Uncertainty Based Heuristic for High Dimensional Feature Selection.	es
dc.type	research article	es
dc.description.fundingtext	PROCIENCIA	es
dc.journal.title	Entropy 2021: The Scientific Tool of the 21st Century	es
dc.relation.projectCONACYT	PINV18-1199	es
dc.rights.accessRights	open access	es

Artículos científicos
La colección comprende artículos científicos, revisiones y artículos de conferencia que son resultados de actividades científicas y de innovación financiadas por los programas PROCIENCIA y PROINNOVA.