An improved algorithm for normal parameter reduction of soft set

Article type: Research Article

Authors: Khan, Abid^* | Zhu, Yuanguo

Affiliations: School of Science, Nanjing University of Science and Technology, Nanjing, China

Correspondence: [*] Corresponding author. Abid Khan, School of Science, Nanjing University of Science and Technology, Nanjing 210094, China. E-mail: [email protected]

Abstract: The normal parameter reduction is used as a useful approach to identify the irrelevant parameters in soft set-based decision making systems. It finds a subset with least number of parameters that preserve the original classification of the decision alternatives. A number of algorithms have been developed for the normal parameter reduction of soft set but the case of repeated columns (i.e., ei = ej) was only considered by Danjuma et al. In this study, first we address the limitations of the Danjuma et al.’s approach to normal parameter reduction of soft set. Then, we propose a new algorithm for normal parameter reduction of soft set which is free of all such limitations. Moreover, we compare the proposed algorithm with some of the existing algorithms of normal parameter reduction of soft set to show its efficiency. Finally, the application of the proposed algorithm is elaborated by a medical diagnostic problem.

Keywords: Soft set, normal parameter reduction, decision making, medical diagnosis

DOI: 10.3233/JIFS-190071

Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2953-2968, 2019