As machine learning (ML) continues to advance in the field of materials science, the
variation in strategies for the same steps of the ML workflow becomes increasingly …

Accurate and reliable traffic flow data are essential for intelligent transportation systems;
however, limitations arising from hardware and communication costs often lead to missing …

Monotone missing data is a common problem in data analysis. However, imputation
combined with dimensionality reduction can be computationally expensive, especially with …

For many use cases, combining information from different datasets can be of interest to
improve a machine learning model's performance, especially when the number of samples …

Missing data is a common problem in practical settings. Various imputation methods have
been developed to deal with missing data. However, even though the label is usually …

Multivariate time-series data are abundant in many application areas, such as finance,
transportation, environment, and healthcare. However, for many reasons, missing data …

Public multimedia datasets can enhance knowledge discovery and model development as
more researchers have the opportunity to contribute to exploring them. However, as these …

Correlation matrix visualization is essential for understanding the relationships between
variables in a dataset, but missing data can seriously affect this important data visualization …

Oversampling and imputation for imbalanced missing data Imbalanced data is a widespread
issue that is naturally occurring. For instance, fraudulent banking transactions are less …

Missing values are a common phenomenon encountered in datasets, posing challenges to
data analysis. Thus, it becomes important to employ effective methods for imputing missing …