Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …

Missing data are a universal data quality problem in many domains, leading to misleading
analysis and inaccurate decisions. Much research has been done to investigate the different …

Bearing fault diagnosis in real-world applications has challenges such as insufficient
labeled data, changing working conditions of the rotary machinery, and missing data due to …

Purpose The primary aim of this study is to review the studies from different dimensions
including type of methods, experimentation setup and evaluation metrics used in the novel …

Missing data is quite common in the industrial field, resulting in problems in downstream
applications, as most data driven methods used in these applications rely on complete and …

Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

Missing data is a common problem in real-world data sets and it is amongst the most
complex topics in computer science and many other research domains. The common ways …

The real-world data analysis and processing using data mining techniques often are facing
observations that contain missing values. The main challenge of mining datasets is the …

Abstract Information quality has received extensive attention recently. Yager and Petry
proposed an information quality suitable for the framework of probability theory, and …

In reality, the missing of some traffic data is inevitable due to some unexpected errors, which
not only affects traffic management but also hinders the development of traffic data research …