Dominance-based rough approximation discovers inconsistencies from ordered criteria and
satisfies the requirement of the dominance principle between single-valued domains of …

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 …

Medical decision-making with high-dimensional complex data has recently become a focus
and difficulty in artificial intelligence and the medical field. Tumor diagnosis using data …

The approximation space in rough set theory is important for dealing with uncertainties. As
the information contained in various information systems is constantly updated and changed …

This study is concerned with the design of a three-way classification mechanism realized
through combing fuzzy decision trees and expressing uncertainty associated with …

Interval-valued data describe the random phenomenon that abounds in the real world, a
pivotal research orientation in uncertainty processing. With the rapid development of big …

The probabilistic rough set (PRS) model, through the incorporation of error levels,
represents a quantitative extension of the classical rough set model. It serves as a …

Variable precision multigranulation fuzzy rough sets (VP-MFRSs) use two direct integrations:
the multigranulation maximum and minimum. Their optimistic and pessimistic models …

Forest fires are an abrupt and highly destructive meteorological disaster that can occur in all
regions of the world, resulting in significant ecological, economic and social losses …

Fuzzy phenomena exist widely in real life, and with the rapid development of big data
technology we may gather information from multiple sources. So it is extremely meaningful …