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dc.contributor.authorPrat, Gabriel
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-12-01T10:15:32Z
dc.date.available2016-12-01T10:15:32Z
dc.date.issued2016
dc.identifier.citationPrat, G., Belanche, Ll. Instance and feature weighted k-nearest-neighbors algorithm. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016". I6doc.com, 2016, p. 605-610.
dc.identifier.isbn978-287587027-8
dc.identifier.urihttp://hdl.handle.net/2117/97582
dc.description.abstractWe present a novel method that aims at providing a more stable selection of feature subsets when variations in the training process occur. This is accomplished by using an instance-weighting process -assigning different importances to instances as a preprocessing step to a feature weighting method that is independent of the learner, and then making good use of both sets of computed weigths in a standard Nearest-Neighbours classifier. We report extensive experimentation in well-known benchmarking datasets as well as some challenging microarray gene expression problems. Our results show increases in stability for most subset sizes and most problems, without compromising prediction accuracy.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherI6doc.com
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshNeural networks (Computer science)
dc.subject.otherGene expression
dc.subject.otherFeature subset
dc.subject.otherFeature weighting
dc.subject.otherInstance weighting
dc.subject.otherMicroarray gene expression
dc.subject.otherNearest neighbour
dc.subject.otherPre-processing step
dc.subject.otherPrediction accuracy
dc.subject.otherTraining process
dc.titleInstance and feature weighted k-nearest-neighbors algorithm
dc.typeConference report
dc.subject.lemacXarxes neuronals (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-178.pdf
dc.rights.accessOpen Access
local.identifier.drac19287328
dc.description.versionPostprint (published version)
local.citation.authorPrat, G.; Belanche, Ll.
local.citation.contributorEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
local.citation.publicationNameESANN 2016 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 27-29 April 2016
local.citation.startingPage605
local.citation.endingPage610


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