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dc.contributor.authorUrkullu Villanueva, Ari
dc.contributor.authorPérez Martínez, Aritz
dc.contributor.authorCalvo Molinos, Borja
dc.date.accessioned2017-10-30T17:04:33Z
dc.date.available2017-10-30T17:04:33Z
dc.date.issued2017-10-25
dc.identifier.urihttp://hdl.handle.net/10810/23240
dc.description.abstractRecently, concerns about the reproducibility of scientific studies have been growing among the scientific community, mainly due to the existing large quantity of irreproducible results. This has reach such an extent that a perception of a reproducibility crisis has spread through the scientific community (Baker, 2016). Among others, researchers point out “insufficient replication in the lab, poor oversight or low statistical power” as the reasons behind this crisis. Indeed, the A.S.A. warned almost two years ago that the problem derived from an inappropriate use of some statistical tools (Wasserstein & Lazar, 2016). Motivated to work on this reproducibility problem, in this paper we present a framework that allows to model the reproducibility in ranking based feature subset selection problems. In that context, among n features that could be relevant for a given objective, an attempt is made to choose the best subset of a prefixed size i ∈ {1,..., n} through a method capable of ranking the features. In this situation, we will analyze the reproducibility of a given method which is defined as the consistency of the selection in different repetitions of the same experiment.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectReproducibilityes_ES
dc.subjectFeature selectiones_ES
dc.subjectUrn modeles_ES
dc.subjectWallenius noncentral hypergeometric distributiones_ES
dc.titleStatistical model for the reproducibility in ranking based feature selectiones_ES
dc.typeinfo:eu-repo/semantics/reportes_ES
dc.departamentoesCiencia de la computación e inteligencia artificiales
dc.departamentoeuKonputazio zientziak eta adimen artifizialaeu


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