Abstract:

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 methods are among these methods. Although deep learning 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 auto-encoder and its denoising and stacked variants. In this study, the missing value in three different real-world datasets was estimated by using denoising auto-encoder (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 methods in estimating the missing values.

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