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A simple and efficient method for variable ranking according to their usefulness for learning

dc.contributor.authorQuevedo Pérez, José Ramón 
dc.contributor.authorBahamonde Rionda, Antonio 
dc.contributor.authorLuaces Rodríguez, Óscar 
dc.date.accessioned2015-04-28T09:19:43Z
dc.date.available2015-04-28T09:19:43Z
dc.date.issued2007
dc.identifier.citationComputational Statistics & Data Analysis, 52(1), p. 578–595 (2007); doi:10.1016/j.csda.2007.02.003
dc.identifier.issn0167-9473
dc.identifier.urihttp://hdl.handle.net/10651/30706
dc.description.abstractThe selection of a subset of input variables is often based on the previous construction of a ranking to order the variables according to a given criterion of relevancy. The objective is then to linearize the search, estimating the quality of subsets containing the topmost ranked variables. An algorithm devised to rank input variables according to their usefulness in the context of a learning task is presented. This algorithm is the result of a combination of simple and classical techniques, like correlation and orthogonalization, which allow the construction of a fast algorithm that also deals explicitly with redundancy. Additionally, the proposed ranker is endowed with a simple polynomial expansion of the input variables to cope with nonlinear problems. The comparison with some state-of-the-art rankers showed that this combination of simple components is able to yield high-quality rankings of input variables. The experimental validation is made on a wide range of artificial data sets and the quality of the rankings is assessed using a ROC-inspired setting, to avoid biased estimations due to any particular learning algorithmspa
dc.format.extentp. 578-595spa
dc.language.isoengspa
dc.publisherElsevier
dc.relation.ispartofComputational Statistics & Data Analysis, 52(1)spa
dc.rights© Elsevier, 2007
dc.subjectVariable rankingspa
dc.subjectDimensionalityreductionspa
dc.titleA simple and efficient method for variable ranking according to their usefulness for learningeng
dc.typejournal article
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.csda.2007.02.003spa
dc.rights.accessRightsopen accessspa


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