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dc.contributor.authorSalmerón Cerdán, Antonio 
dc.contributor.authorMadsen, Anders L.
dc.contributor.authorJensen, Frank
dc.contributor.authorLangseth, Helge 
dc.contributor.authorNielsen, Thomas D.
dc.contributor.authorRamos López, Darío 
dc.contributor.authorMartínez, Ana M.
dc.contributor.authorMasegosa, Andrés R.
dc.date.accessioned2017-07-17T10:48:34Z
dc.date.available2017-07-17T10:48:34Z
dc.date.issued2016
dc.identifier.citationThe final publication is available at IOS Press through http://dx.doi.org/10.3233/978-1-61499-672-9-743es_ES
dc.identifier.urihttp://hdl.handle.net/10835/4916
dc.description.abstractIn this paper we propose a method for scaling up filterbased feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculations between the available computing units is determined based on balanced incomplete block designs, a strategy first developed within the area of statistical design of experiments. We show the scalability of our method through a series of experiments on synthetic and real-world datasets.es_ES
dc.language.isoenes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBandpass filterses_ES
dc.subjectDesign of experimentses_ES
dc.subjectBalanced incomplete block designes_ES
dc.subjectComputing unitses_ES
dc.subjectConditional mutual informationes_ES
dc.subjectFilter-basedes_ES
dc.subjectReal-world datasetses_ES
dc.subjectScaling-upes_ES
dc.subjectStatistical design of experimentses_ES
dc.titleParallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3233/978-1-61499-672-9-743


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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