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dc.contributor.authorBaşeğmez, Hülya
dc.contributor.authorSezer, Emrah
dc.date.accessioned2023-02-21T10:02:26Z
dc.date.available2023-02-21T10:02:26Z
dc.identifier.citationSezer E., Başeğmez H., "An Approach Based on Feature Selection for Missing Value Imputation", International Conference on Intelligent and Fuzzy Systems, INFUS 2021, İstanbul, Türkiye, 24 - 26 Ağustos 2021, cilt.307, ss.945-950
dc.identifier.othervv_1032021
dc.identifier.otherav_3af99fd0-167d-4b51-ad74-038bb3b3f183
dc.identifier.urihttp://hdl.handle.net/20.500.12627/188032
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85115098214&origin=inward
dc.identifier.urihttps://doi.org/10.1007/978-3-030-85626-7_110
dc.description.abstract© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Today, 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.
dc.language.isoeng
dc.subjectMühendislik ve Teknoloji
dc.subjectTELEKOMÜNİKASYON
dc.subjectMÜHENDİSLİK, ELEKTRİK VE ELEKTRONİK
dc.subjectBilgi Sistemleri, Haberleşme ve Kontrol Mühendisliği
dc.subjectKontrol ve Sistem Mühendisliği
dc.subjectSinyal İşleme
dc.subjectBilgisayar Ağları ve İletişim
dc.subjectFizik Bilimleri
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.subjectMühendislik
dc.subjectOTOMASYON & KONTROL SİSTEMLERİ
dc.titleAn Approach Based on Feature Selection for Missing Value Imputation
dc.typeBildiri
dc.contributor.departmentİstanbul Üniversitesi , Fen Bilimleri Enstitüsü , Enformatik Bölümü
dc.identifier.volume307
dc.contributor.firstauthorID4227388


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