Deep Learning-Based Approach for Missing Data Imputation

Basit Öğe Kaydı
dc.contributor.authorCihan, Pınar
dc.date.accessioned2022-05-11T14:15:45Z
dc.date.available2022-05-11T14:15:45Z
dc.date.issued2020
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe missing values in the datasets are a problem that will decrease the machine learning performance. New methods arerecommended every day to overcome this problem. The methods of statistical, machine learning, evolutionary and deeplearning are among these methods. Although deep learning methods is one of the popular subjects of today, there are limitedstudies in the missing data imputation. Several deep learning techniques have been used to handling missing data, one of themis the autoencoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasetswas estimated by using denoising autoencoder (DAE), k-nearest neighbor (kNN) and multivariate imputation by chainedequations (MICE) methods. The estimation success of the methods was compared according to the root mean square error(RMSE) criterion. It was observed that the DAE method was more successful than other statistical methods in estimating themissing values for large datasets.
dc.identifier.doi10.20290/estubtdb.747821
dc.identifier.endpage343
dc.identifier.issn2146-0272
dc.identifier.issn2667-419X
dc.identifier.issue2en_US
dc.identifier.startpage336
dc.identifier.trdizinidTXprd01UZ3dNQT09
dc.identifier.urihttps://doi.org/10.20290/estubtdb.747821
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TXprd01UZ3dNQT09
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6054
dc.identifier.volume8
dc.indekslendigikaynakTR-Dizin
dc.institutionauthorCihan, Pınar
dc.language.isoen
dc.relation.ispartofEskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleDeep Learning-Based Approach for Missing Data Imputation
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
6054.pdf
Boyut:
749.67 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu
Çorlu Mühendislik Fakültesi Koleksiyonu