Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Feature reduction is an important aspect of Big Data analytics on today's ever-larger
datasets. Rough sets are a classical method widely applied in attribute reduction. Most …

Feature selection aims to remove irrelevant or redundant features and thereby remain
relevant or informative features so that it is often preferred for alleviating the dimensionality …

Outliers carry significant information to reflect an anomaly mechanism, so outlier detection
facilitates relevant data mining. In terms of outlier detection, the classical approaches from …

The interaction between features can provide essential information that affects the
performances of learning models. Nevertheless, most feature selection methods do not take …

Measurements of LAN and WAN traffic show that network traffic exhibits variability on
different scales. We examine a mechanism that gives rise to self-similar network traffic and …

Neighborhood rough set theory is widely used to measure the uncertainty of data in machine
learning and data mining. However, the neighborhood radius has a significant influence on …

Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in
current research and industrial applications. Processing massive amounts of data generated …

Attribute reduction is one of the core steps of data analysis. The attribute reduction method
based on neighborhood rough sets (NRS) is widely used. However, the time complexity of …

Attribute reduction is nowadays an extremely important data preprocessing technique in the
field of data mining, which has gained much attention due to its ability to provide better …