Information fusion is capable of fusing and transforming information originated from multiple
sources into an integrated representation. As an important representative of information …

Feature selection methods with antinoise performance are effective dimensionality reduction
methods for classification tasks with noise. However, there are few studies on robust feature …

Rough soft knowledge is a key approach to understand and model uncertain, vague and not
clearly defined situations in a parametric manner. Graphs, hypergraphs and other algebraic …

Fuzzy rough sets have made considerable strides within the domain of machine learning
and data mining and served as a valuable tool for feature selection. However, traditional …

Rough sets offer an efficient mathematical framework to formalize the process of data
analysis and knowledge discovery in the presence of incomplete or uncertain information …

In this study, we attempt to present a three-way multi criteria decision-making (MCDM)
technique with fuzzy soft dominance degree relation based on additive consistency to …

A novel hybrid soft set model called intertemporal hesitant fuzzy soft set is proposed, and
then, it is showed how it can deal with group decision making problems. This model can be …

The classical theory of rough sets (RSs) established by Pawlak, mainly focused on the
approximation of sets characterized by a single equivalence relation (ER) over the universe …

Fuzzy rough set theory offers a valuable approach for addressing data uncertainty; however,
existing fuzzy rough sets often failed to capture data distribution in practical applications due …

The uncertainty in the data is an obstacle in decision-making (DM) problems. In order to
solve problems with a variety of uncertainties a number of useful mathematical approaches …