AbstractIn this paper, we present the concept of fuzzy information granule based on a relatively weaker fuzzy similarity relation called fuzzy TL-similarity relation for the first time. Then, according to the fuzzy information granule, we define the lower and upper approximations of fuzzy sets and a corresponding new fuzzy rough set. Furthermore, we construct a kind of new fuzzy information system based on the fuzzy TL-similarity relation and study its reduction using the fuzzy rough set. At last, we apply the reduction method based on the defined fuzzy rough set in the above fuzzy information system to the reduction of the redundant multiple fuzzy rule in the scheduling problems, and numerical computational results show that the reduction ...
In this paper, first, relationship between bipolar- valued fuzzy set and fuzzy set with its extensio...
Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data p...
In real-world approximation problems, precise input data are economically expensive. Therefore, fuzz...
AbstractIn this paper, we present the concept of fuzzy information granule based on a relatively wea...
AbstractIn this paper, we study the fuzzy reasoning based on a new fuzzy rough set. First, we define...
AbstractIn this paper, we study the fuzzy reasoning based on a new fuzzy rough set. First, we define...
AbstractIn this paper, the theoretical foundation of fuzzy reasoning is analyzed, and the idea that ...
In practice, some of information systems are based on dominance relations, and values of decision at...
In recent years, rough set theory has been considered as a strong solution to solve artificial intel...
In this paper, we present some new ideas for fuzzy reasoning from the viewpoint of fuzzy rough set. ...
The information systems with incomplete attribute values and fuzzy decisions commonly exist in pract...
AbstractThe concept of the rough set was originally proposed by Pawlak as a formal tool for modeling...
Rough set theory is a powerful tool to analysis the information systems. Fuzzy rough set is introduc...
Rough set theory is a powerful tool to analysis the information systems. Fuzzy rough set is introduc...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...
In this paper, first, relationship between bipolar- valued fuzzy set and fuzzy set with its extensio...
Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data p...
In real-world approximation problems, precise input data are economically expensive. Therefore, fuzz...
AbstractIn this paper, we present the concept of fuzzy information granule based on a relatively wea...
AbstractIn this paper, we study the fuzzy reasoning based on a new fuzzy rough set. First, we define...
AbstractIn this paper, we study the fuzzy reasoning based on a new fuzzy rough set. First, we define...
AbstractIn this paper, the theoretical foundation of fuzzy reasoning is analyzed, and the idea that ...
In practice, some of information systems are based on dominance relations, and values of decision at...
In recent years, rough set theory has been considered as a strong solution to solve artificial intel...
In this paper, we present some new ideas for fuzzy reasoning from the viewpoint of fuzzy rough set. ...
The information systems with incomplete attribute values and fuzzy decisions commonly exist in pract...
AbstractThe concept of the rough set was originally proposed by Pawlak as a formal tool for modeling...
Rough set theory is a powerful tool to analysis the information systems. Fuzzy rough set is introduc...
Rough set theory is a powerful tool to analysis the information systems. Fuzzy rough set is introduc...
Abstract—Rough set theory is a popular and powerful machine learning tool. It is especially suitable...
In this paper, first, relationship between bipolar- valued fuzzy set and fuzzy set with its extensio...
Modern science is increasingly data-driven and collaborative in nature. Comparing to ordinary data p...
In real-world approximation problems, precise input data are economically expensive. Therefore, fuzz...