Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis

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but.event.date26.04.2022cs
but.event.titleSTUDENT EEICT 2022cs
dc.contributor.authorKostova, A.
dc.contributor.authorSchwarzerova, J.
dc.date.accessioned2023-04-25T10:17:06Z
dc.date.available2023-04-25T10:17:06Z
dc.date.issued2022cs
dc.description.abstractCurrently, one of the most challenges in data analysis is connected to prediction modeling including dynamic information. Metabolomics analysis focuses on data presented dynamic information in real-time such as time-series data. Unfortunately, prediction models based on time series data are often affected by a phenomenon called concept drift. This phenomenon can reduce the accuracy of prediction models which is an unwanted effect. On the other hand, concept drift analysis can be useful in finding confounding factors. This study is divided into two parts. The first part presents the modeling of prediction classifiers based on metabolite data. The second part of this study brings concept drift detection in the created classified models. This study presented approaches to identify one of the confounding factors in human biology.en
dc.formattextcs
dc.format.extent128-131cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings I of the 28st Conference STUDENT EEICT 2022: General papers. s. 128-131. ISBN 978-80-214-6029-4cs
dc.identifier.isbn978-80-214-6029-4
dc.identifier.urihttp://hdl.handle.net/11012/209308
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings I of the 28st Conference STUDENT EEICT 2022: General papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectConcept driften
dc.subjectConcept drift detectionen
dc.subjectMetabolomicsen
dc.subjectMachine learningen
dc.subjectPrediction modelingen
dc.titleConcept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysisen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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