Attribute reduction is one of the most important applications of fuzzy rough sets in machine
learning and pattern recognition. Most existing methods employ the intersection operation of …

Feature selection is viewed as an important preprocessing step for pattern recognition,
machine learning, and data mining. Neighborhood is one of the most important concepts in …

Display Omitted We focused on the clustering problem in picture fuzzy sets. A generalized
picture distance measure was proposed. A novel hierarchical picture clustering method …

Rough set theory has been extensively discussed in machine learning and pattern
recognition. It provides us another important theoretical tool for feature selection. In this …

Attribute subset selection is an important issue in data mining and information processing.
However, most automatic methodologies consider only the relevance factor between …

The primitive notions of rough sets and intuitionistic fuzzy set (IFS) are general mathematical
tools having the ability to handle the uncertain and imprecise knowledge easily. EDA …

Attribute reduction plays an important role in the areas of rough sets and granular
computing. Many kinds of attribute reducts have been defined in previous studies. However …

This paper proposes some new geometric operations on intuitionistic fuzzy sets (IFSs)
based on probability non-membership (PN) function operator, probability membership (PM) …

Due to the increasing awareness of environmental and social issues, sustainable supplier
selection (SSS) becomes an important problem. In order to scientifically evaluation the SSS …

Exploring rough sets from the perspective of multigranulation represents a promising
direction in rough set theory, where concepts are approximated by multiple granular …