Missing data is a problem often found in real-world datasets and it can degrade the
performance of most machine learning models. Several deep learning techniques have …

Missing data consists in the lack of information in a dataset and since it directly influences
classification performance, neglecting it is not a valid option. Over the years, several studies …

Dealing with missing data in data analysis is inevitable. Although powerful imputation
methods that address this problem exist, there is still much room for improvement. In this …

Deep learning models have been recently proposed in the applications of missing data
imputation. In this paper, we review the popular statistical, machine learning, and deep …

Many efforts have been dedicated to addressing data loss in various domains. While task-
specific solutions may eliminate the respective issue in certain applications, finding a …

Missing data is a significant problem impacting all domains. State-of-the-art framework for
minimizing missing data bias is multiple imputation, for which the choice of an imputation …

Electronic health records (EHR) are patient-level information, eg, laboratory tests and
questionnaires, stored in electronic format. Compared to physical records, the EHR …

With the increasing importance and complexity of data pipelines, data quality became one of
the key challenges in modern software applications. The importance of data quality has …

Datasets with missing values are very common on industry applications, and they can have
a negative impact on machine learning models. Recent studies introduced solutions to the …

Often real-world datasets are incomplete and contain some missing attribute values.
Furthermore, many data mining and machine learning techniques cannot directly handle …