Handling missing data through deep convolutional neural network

The presence of missing data is a challenging issue in processing real-world datasets. It is necessary to improve the data quality by imputing the missing values so that effective learning from data can be achieved. Recently, deep learning has become the most powerful type of machine learning techniques, which can be used for discovering the hidden knowledge that exists in a large dataset to make accurate predictions. In this paper, we propose an imputation method that involves using a convolutional neural network to impute the missing values. The missing value of each instance is imputed essentially by using a trained kernel. The weights of the kernel are determined by learning from the given data that are arranged spatially in the data matrix. The kernel carries out a weighted sum of neighboring elements in an array for imputing the missing values. In addition, in the absence of the true values with which the missing values are expected to be replaced, a loss function is designed without the need to know the true value. Our method is evaluated on UCI datasets in comparison with state-of-the-art methods. The experimental results show that the proposed approach performs closely to or better than other methods. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

Dealing with missing data in real-world datasets is a challenging issue, and it is crucial to improve data quality by filling in missing values for effective learning. This paper proposes a method that uses convolutional neural networks to impute missing values, which shows comparable or better performance compared to other methods.