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dc.contributor.authorBilalli, Besim
dc.contributor.authorAbelló Gamazo, Alberto
dc.contributor.authorAluja Banet, Tomàs
dc.contributor.authorMunir, Rana Faisal
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.accessioned2019-01-31T10:45:47Z
dc.date.available2019-07-01T08:05:30Z
dc.date.issued2019
dc.identifier.citationBilalli, B. [et al.]. PRESISTANT : data pre-processing assistant. A: International Conference on Advanced Information Systems Engineering. "Information Systems in the Big Data Era: CAiSE Forum 2018, Tallinn, Estonia, June 11-15, 2018: proceedings". Berlín: Springer, 2019, p. 57-65.
dc.identifier.isbn978-3-319-92900-2
dc.identifier.urihttp://hdl.handle.net/2117/127984
dc.description.abstractA concrete classification 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. Typically, in order to improve the results, datasets need to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and non-experienced users become overwhelmed. Trial and error is not feasible in the presence of big amounts of data. We developed a method and tool—PRESISTANT, with the aim of answering the need for user assistance during data pre-processing. Leveraging ideas from meta-learning, PRESISTANT is capable of assisting the user by recommending pre-processing operators that ultimately improve the classification performance. The user selects a classification algorithm, from the ones considered, and then PRESISTANT proposes candidate transformations to improve the result of the analysis. In the demonstration, participants will experience, at first hand, how PRESISTANT easily and effectively ranks the pre-processing operators.
dc.format.extent9 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject.lcshData mining
dc.subject.otherData pre-processing
dc.subject.otherMeta-learning
dc.subject.otherData mining
dc.titlePRESISTANT : data pre-processing assistant
dc.typeConference lecture
dc.subject.lemacMineria de dades
dc.subject.lemacMETA LEARNING
dc.contributor.groupUniversitat Politècnica de Catalunya. inSSIDE - integrated Software, Service, Information and Data Engineering
dc.contributor.groupUniversitat Politècnica de Catalunya. LIAM - Laboratori de Modelització i Anàlisi de la Informació
dc.identifier.doi10.1007/978-3-319-92901-9
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-319-92901-9_6
dc.rights.accessOpen Access
local.identifier.drac23648533
dc.description.versionPostprint (author's final draft)
local.citation.authorBilalli, B.; Abello, A.; Aluja, T.; Munir, R.; Wrembel, R.
local.citation.contributorInternational Conference on Advanced Information Systems Engineering
local.citation.pubplaceBerlín
local.citation.publicationNameInformation Systems in the Big Data Era: CAiSE Forum 2018, Tallinn, Estonia, June 11-15, 2018: proceedings
local.citation.startingPage57
local.citation.endingPage65


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