DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION

Cihan, Pınar

doi:10.20290/estubtdb.747821

DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION

Pınar CİHAN (Tekirdağ Namık Kemal Üniversitesi, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği, Tekirdağ, Türkiye)

Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler

5 1

Yıl: 2020 Cilt: 8 Sayı: 2 Sayfa Aralığı: 336 - 343 Metin Dili: İngilizce DOI: 10.20290/estubtdb.747821 İndeks Tarihi: 08-01-2021

DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION

Öz:

The 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.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	Cihan P (2020). DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. , 336 - 343. 10.20290/estubtdb.747821
Chicago	Cihan Pınar DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. (2020): 336 - 343. 10.20290/estubtdb.747821
MLA	Cihan Pınar DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. , 2020, ss.336 - 343. 10.20290/estubtdb.747821
AMA	Cihan P DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. . 2020; 336 - 343. 10.20290/estubtdb.747821
Vancouver	Cihan P DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. . 2020; 336 - 343. 10.20290/estubtdb.747821
IEEE	Cihan P "DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION." , ss.336 - 343, 2020. 10.20290/estubtdb.747821
ISNAD	Cihan, Pınar. "DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION". (2020), 336-343. https://doi.org/10.20290/estubtdb.747821

APA	Cihan P (2020). DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler, 8(2), 336 - 343. 10.20290/estubtdb.747821
Chicago	Cihan Pınar DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler 8, no.2 (2020): 336 - 343. 10.20290/estubtdb.747821
MLA	Cihan Pınar DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler, vol.8, no.2, 2020, ss.336 - 343. 10.20290/estubtdb.747821
AMA	Cihan P DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler. 2020; 8(2): 336 - 343. 10.20290/estubtdb.747821
Vancouver	Cihan P DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION. Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler. 2020; 8(2): 336 - 343. 10.20290/estubtdb.747821
IEEE	Cihan P "DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION." Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler, 8, ss.336 - 343, 2020. 10.20290/estubtdb.747821
ISNAD	Cihan, Pınar. "DEEP LEARNING-BASED APPROACH FOR MISSING DATA IMPUTATION". Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi b- Teorik Bilimler 8/2 (2020), 336-343. https://doi.org/10.20290/estubtdb.747821