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Parameter reductions in N ‐soft sets and their applications in decision‐making
Author(s) -
Akram Muhammad,
Ali Ghous,
Alcantud José C. R.,
Fatimah Fatia
Publication year - 2021
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12601
Subject(s) - soft set , reduction (mathematics) , rough set , computer science , reduct , set (abstract data type) , set theory , order (exchange) , soft computing , mathematical optimization , mathematics , data mining , artificial intelligence , artificial neural network , geometry , finance , economics , programming language , fuzzy logic
Parameter reduction is an important operation for improving the performance of decision‐making processes in various uncertainty theories. The theory of N ‐soft sets is emerging as a powerful mathematical tool for dealing with uncertainties beyond the standard formulation of the soft set theory. In this research article, we extend the notion of parameter reduction to N ‐soft set theory, and we also justify its practical calculation. To this purpose, we define related theoretical concepts (e.g. N ‐soft subset, reduct N ‐soft set and redundant parameter) and examine some of their fundamental properties. Then, we argue that the idea of attributes reduction from the rough set theory cannot be employed in the N ‐soft set theory in order to reduce the number of parameters. Consequently, we take an original position in order to adequately define and compute parameter reductions in N ‐soft sets. Finally, we develop an application of parameter reduction of N ‐soft sets.