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dc.contributor.authorKobayashi, Vladimer
dc.contributor.authorAluja Banet, Tomàs
dc.contributor.authorBelanche Muñoz, Luis Antonio
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.date.accessioned2014-03-17T12:22:53Z
dc.date.created2013
dc.date.issued2013
dc.identifier.citationKobayashi, V.; Aluja, T.; Belanche, Ll. Handling missing values in kernel methods with application to microbiology data. A: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. "ESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24-26 April 2013". Bruges: 2013, p. 397-402.
dc.identifier.isbn978-2-87419-081-0
dc.identifier.urihttp://hdl.handle.net/2117/22102
dc.description.abstractWe discuss several approaches that make possible for kernel methods to deal with missing values. The first two are extended kernels able to handle missing values without data preprocessing methods. Another two methods are derived from a sophisticated multiple imputation technique involving logistic regression as local model learner. The performance of these approaches is compared using a binary data set that arises typically in microbiology (the microbial source tracking problem). Our results show that the kernel extensions demonstrate competitive performance in comparison with multiple imputation in terms of predictive accuracy. However, these results are achieved with a simpler and deterministic methodology and entail a much lower computational effort.
dc.format.extent6 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Anàlisi matemàtica
dc.subject.lcshIntegral equations
dc.titleHandling missing values in kernel methods with application to microbiology data
dc.typeConference report
dc.subject.lemacAnàlisi matemàtica
dc.contributor.groupUniversitat Politècnica de Catalunya. LIAM - Laboratori de Modelització i Anàlisi de la Informació
dc.contributor.groupUniversitat Politècnica de Catalunya. SOCO - Soft Computing
dc.description.peerreviewedPeer Reviewed
dc.subject.inspec45H05 Miscellaneous special kernels
dc.relation.publisherversionhttps://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2013
dc.rights.accessOpen Access
local.identifier.drac12908997
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorKobayashi, V.; Aluja, T.; Belanche, Ll.
local.citation.contributorEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
local.citation.publicationNameESANN 2013 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges (Belgium), 24-26 April 2013
local.citation.startingPage397
local.citation.endingPage402


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