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dc.contributor.authorUrkullu, A.
dc.contributor.authorPérez, A. 
dc.contributor.authorCalvo, B.
dc.date.accessioned2021-06-09T06:52:14Z
dc.date.available2021-06-09T06:52:14Z
dc.date.issued2020-11-05
dc.identifier.issn0219-3116
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1295
dc.description.abstractThe stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the reproducibility of ranking-based feature subset selection algorithms. When applied to data, this family of algorithms builds an ordering of variables in terms of a measure of relevance. In order to quantify the reproducibility of ranking-based feature subset selection algorithms, we propose a model that takes into account all the different sized subsets of top-ranked features. The model is fitted to data through the minimization of an error function related to the expected values of Kuncheva’s consistency index for those subsets. Once it is fitted, the model provides practical information about the feature subset selection algorithm analyzed, such as a measure of its expected reproducibility or its estimated area under the receiver operating characteristic curve regarding the identification of relevant features. We test our model empirically using both synthetic and a wide range of real data. The results show that our proposal can be used to analyze feature subset selection algorithms based on rankings in terms of their reproducibility and their performance.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectFeature selectionen_US
dc.subjectStabilityen_US
dc.subjectReproducibilityen_US
dc.subjectHigh dimensionalityen_US
dc.titleStatistical model for reproducibility in ranking-based feature selectionen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s10115-020-01519-3en_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/1PE/TIN2017-82626-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleKnowledge and Information Systemsen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España