Probabilistic approaches have been applied to the theory of rough set in several forms,
including decision-theoretic analysis, variable precision analysis, and information-theoretic …

For heterogeneous data sets containing numerical and symbolic feature values, feature
selection based on fuzzy neighborhood multigranulation rough sets (FNMRS) is a very …

Gene expression data classification is an important technology for cancer diagnosis in
bioinformatics and has been widely researched. Due to the large number of genes and the …

Over the years conventional neural networks has shown state-of-art performance on many
problems. However, their performance on recognition system is still not widely accepted in …

For incomplete data with mixed numerical and symbolic attributes, attribute reduction based
on neighborhood multi-granulation rough sets (NMRS) is an important method to improve …

Granular-ball computing (GBC) is an efficient, robust, and scalable learning method for
granular computing. The granular ball (GB) generation method is based on GB computing …

Many methods based on the rough set to deal with incomplete information systems have
been proposed in recent years. However, they are only suitable for the incomplete systems …

Attribute reduction is a basic issue in knowledge representation and data mining. This paper
deals with attribute reduction in incomplete information systems and incomplete decision …

Feature selection in large, incomplete decision systems is a challenging problem. To avoid
exponential computation in exhaustive feature selection methods, many heuristic feature …

In rough set theory, a decision class (ie, a subset of objects) is approximated by three pair-
wise disjoint positive, boundary, and negative regions. The concept of three-way decisions …