Rough set theory, as an academic hotspot in the field of artificial intelligence, has provided a
solid theoretical foundation for feature selection. However, with the continuous updating of …

Dominance-based neighborhood rough set (DNRS) is capable to give qualitative and
quantitative descriptions of the relations between ordered objects. In spite of its effectiveness …

Feature selection is a key issue in the research on rough set theory. However, when
handling large-scale data, many current feature selection methods based on rough set …

Rough set theory has been proven to be an effective tool to feature subset selection. Current
research usually employ hill-climbing as search strategy to select feature subset. However …

Rough set theory has been successfully applied to many fields, such as data mining, pattern
recognition, and machine learning. Kernel rough sets and neighborhood rough sets are two …

Neighborhood rough set is an important model in feature selection. However, it only
determines the granularity of the neighborhood from a feature perspective, while ignoring …

Attribute selection is one of the important problems encountered in pattern recognition,
machine learning, data mining, and bioinformatics. It refers to the problem of selecting those …

Feature selection is the process of selecting important features from a dataset. The feature
subset formed by important features represents the features of the entire dataset to reduce …

Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Data is growing at an exponential pace. To cope with this data explosion, we need effective
data processing and analysis techniques. Feature selection is selecting a subset of features …