An Approach Based on Feature Selection for Missing Value Imputation

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dc.contributor.authorSezer E.
dc.contributor.authorBaşeğmez H.
dc.date.accessioned2024-03-13T10:01:03Z
dc.date.available2024-03-13T10:01:03Z
dc.date.issued2022
dc.departmentİstanbul Beykent Üniversitesien_US
dc.descriptionInternational Conference on Intelligent and Fuzzy Systems, INFUS 2021 -- 24 August 2021 through 26 August 2021 -- -- 264409en_US
dc.description.abstractToday, with the spread of technologies such as the internet of things and data acquisition from sensors, the data obtained has increased. The size of the data produced by other sources, especially digital platforms, is increasing day by day. This increase in data production enables the development of effective artificial intelligence applications and in-depth analysis. However, in many data collection processes, missing values are included in the data set due to operational problems or different reasons. This situation is expressed as a data quality problem in the literature. It is possible that the analysis to be made on this data will be negatively affected by this situation. Various statistical techniques and machine learning-based techniques exist in the literature for filling missing values. In this study, an approach is put forward that suggests missing values imputation based on the consistency of the sample with missing values with other samples in the data set. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-030-85626-7_110
dc.identifier.endpage950en_US
dc.identifier.isbn9783030856250
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85115098214
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage945en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-85626-7_110
dc.identifier.urihttps://hdl.handle.net/20.500.12662/2945
dc.identifier.volume307en_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConsistency Baseden_US
dc.subjectImputationen_US
dc.subjectMissing valuesen_US
dc.titleAn Approach Based on Feature Selection for Missing Value Imputationen_US
dc.typeConference Objecten_US

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