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dc.contributor.authorBilalli, Besim
dc.contributor.authorAbelló Gamazo, Alberto
dc.contributor.authorAluja Banet, Tomàs
dc.contributor.authorWrembel, Robert
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Serveis i Sistemes d'Informació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.date.accessioned2017-04-04T10:29:27Z
dc.date.available2017-04-04T10:29:27Z
dc.date.issued2016
dc.identifier.citationBilalli, B., Abello, A., Aluja, T., Wrembel, R. Automated data pre-processing via meta-learning. A: International Conference on Model and Data Engineering. "Model and Data Engineering - 6th International Conference, MEDI 2016, Proceedings". Almeria: 2016, p. 194-208.
dc.identifier.isbn9783319455464
dc.identifier.urihttp://hdl.handle.net/2117/103255
dc.descriptionThe final publication is available at link.springer.com
dc.description.abstractA data mining algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. As a matter of fact, a dataset usually needs to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and nonexperienced users become overwhelmed. We show that this problem can be addressed by an automated approach, leveraging ideas from metalearning. Specifically, we consider a wide range of data pre-processing techniques and a set of data mining algorithms. For each data mining algorithm and selected dataset, we are able to predict the transformations that improve the result of the algorithm on the respective dataset. Our approach will help non-expert users to more effectively identify the transformations appropriate to their applications, and hence to achieve improved results.
dc.format.extent15 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Enginyeria del software
dc.subject.lcshData mining -- Statistical methods
dc.subject.otherData handling
dc.subject.otherAutomated approach
dc.subject.otherAutomated data
dc.subject.otherCategorical attributes
dc.subject.otherContinuous attribute
dc.subject.otherData mining algorithm
dc.subject.otherData preprocessing
dc.subject.otherExpert users
dc.subject.otherPre-processing
dc.titleAutomated data pre-processing via meta-learning
dc.typeConference report
dc.subject.lemacMineria de dades -- Mètodes estadístics
dc.contributor.groupUniversitat Politècnica de Catalunya. MPI - Modelització i Processament de la Informació
dc.contributor.groupUniversitat Politècnica de Catalunya. LIAM - Laboratori de Modelització i Anàlisi de la Informació
dc.identifier.doi10.1007/978-3-319-45547-1_16
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-319-45547-1_16
dc.rights.accessOpen Access
local.identifier.drac19102972
dc.description.versionPostprint (published version)
local.citation.authorBilalli, B.; Abello, A.; Aluja, T.; Wrembel, R.
local.citation.contributorInternational Conference on Model and Data Engineering
local.citation.pubplaceAlmeria
local.citation.publicationNameModel and Data Engineering - 6th International Conference, MEDI 2016, Proceedings
local.citation.startingPage194
local.citation.endingPage208


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