Feature selection based on neighborhood rough set is a noteworthy step in dealing with
numerical data. Information entropy, proven in many theoretical analysis and practical …

The multi-view learning is a fundamental problem in the multimedia analysis. However, most
existing multi-view learning methods need to calculate a similarity matrix for each view. This …

Incremental attribute reduction aims to improve the efficiency of obtaining reduct from the
dynamic data, which has attracted more and more attention. Nevertheless, the existing …

The relative decision self-information is a crucial evaluation function of feature selection in
information system. It encapsulates classification information in upper and lower …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Granular structures are fundamental components of human granulation intelligence and
different views or scales of granulation result in different granular structures. Therefore, the …

Multisource information fusion is an important big data technology that plays a crucial role in
the fields of data mining and knowledge discovery. Multigranulation information fusion is an …

For incomplete datasets with mixed numerical and symbolic features, feature selection
based on neighborhood multi-granulation rough sets (NMRS) is developing rapidly …

As a commonly used framework for uncertainty reasoning, tolerance rough set has achieved
remarkable success in handling incomplete information systems with missing values. Three …

As intelligent systems continue to evolve, problems are becoming increasingly complex. The
constant abundance of data puts a higher demand on the value of data utilization. Granular …