The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information.It can be characterized by two crisp sets,named the upper and lower approximations that are used to de...The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information.It can be characterized by two crisp sets,named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset.This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space MSAS via ideal J.The novel approach(indicated by JMSAS)modifies the approximation space to diminish the bound-ary region and enhance the measure of accuracy.The suggested method is more accurate than Pawlak’s and EL-Sharkasy techniques.Via illustrated examples,several remarkable results using these notions are obtained and some of their properties are established.Several sorts of near open(resp.closed)sets based on JMSAS are studied.Furthermore,the connections between these assorted kinds of near-open sets in JMSAS are deduced.The advantages and disadvan-tages of the proposed approach compared to previous ones are examined.An algorithm using MATLAB and a framework for decision-making problems are verified.Finally,the chemical application for the classification of amino acids(AAs)is treated to highlight the significance of applying the suggested approximation.展开更多
Approximation space can be said to play a critical role in the accuracy of the set’s approximations.The idea of“approximation space”was introduced by Pawlak in 1982 as a core to describe information or knowledge in...Approximation space can be said to play a critical role in the accuracy of the set’s approximations.The idea of“approximation space”was introduced by Pawlak in 1982 as a core to describe information or knowledge induced from the relationships between objects of the universe.The main objective of this paper is to create new types of rough set models through the use of different neighborhoods generated by a binary relation.New approximations are proposed representing an extension of Pawlak’s rough sets and some of their generalizations,where the precision of these approximations is substantially improved.To elucidate the effectiveness of our approaches,we provide some comparisons between the proposed methods and the previous ones.Finally,we give a medical application of lung cancer disease as well as provide an algorithm which is tested on the basis of hypothetical data in order to compare it with current methods.展开更多
文摘The theory of rough set represents a non-statistical methodology for analyzing ambiguity and imprecise information.It can be characterized by two crisp sets,named the upper and lower approximations that are used to determine the boundary region and accurate measure of any subset.This article endeavors to achieve the best approximation and the highest accuracy degree by using the minimal structure approximation space MSAS via ideal J.The novel approach(indicated by JMSAS)modifies the approximation space to diminish the bound-ary region and enhance the measure of accuracy.The suggested method is more accurate than Pawlak’s and EL-Sharkasy techniques.Via illustrated examples,several remarkable results using these notions are obtained and some of their properties are established.Several sorts of near open(resp.closed)sets based on JMSAS are studied.Furthermore,the connections between these assorted kinds of near-open sets in JMSAS are deduced.The advantages and disadvan-tages of the proposed approach compared to previous ones are examined.An algorithm using MATLAB and a framework for decision-making problems are verified.Finally,the chemical application for the classification of amino acids(AAs)is treated to highlight the significance of applying the suggested approximation.
文摘Approximation space can be said to play a critical role in the accuracy of the set’s approximations.The idea of“approximation space”was introduced by Pawlak in 1982 as a core to describe information or knowledge induced from the relationships between objects of the universe.The main objective of this paper is to create new types of rough set models through the use of different neighborhoods generated by a binary relation.New approximations are proposed representing an extension of Pawlak’s rough sets and some of their generalizations,where the precision of these approximations is substantially improved.To elucidate the effectiveness of our approaches,we provide some comparisons between the proposed methods and the previous ones.Finally,we give a medical application of lung cancer disease as well as provide an algorithm which is tested on the basis of hypothetical data in order to compare it with current methods.
