EKSİK VERİLERİ TAMAMLAMADA DERİN ÖĞRENME TEMELLİ YAKLAŞIM

Pinar CİHAN
2020 Eskişehir Teknik Üniversitesi Bilim ve Teknoloji Dergisi B - Teorik Bilimler  
The missing values in the datasets are a problem that will decrease the machine learning performance. New methods are recommended every day to overcome this problem. The methods of statistical, machine learning, evolutionary and deep learning are among these methods. Although deep learning methods is one of the popular subjects of today, there are limited studies in the missing data imputation. Several deep learning techniques have been used to handling missing data, one of them is the
more » ... hem is the autoencoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasets was estimated by using denoising autoencoder (DAE), k-nearest neighbor (kNN) and multivariate imputation by chained equations (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 the missing values for large datasets.
doi:10.20290/estubtdb.747821 fatcat:jai7mns3j5eypgxb343oao2c74