Parameter reduction methods have been adopted by many scientiﬁc communities since it provides the basic concept for removing irrelevant features and subset of parameters that provides the same descriptive or decisionability as the entire set of parameters. Several reduction approaches have been proposed for fuzzy-soft set in making decision of datasets with fuzzy-soft values.However, existing fuzzy soft set reduction approaches suffer to handle interval-valued intuitionistic fuzzy soft datasets. To overcome this issue, we introduce an adjustable reduction approach of interval-valued intuitionistic fuzzy soft sets (AR-IIFSS) for decision making. The novelty of APR-IIFSS is that we generalize the existing approaches on reduction of fuzzy soft sets (R-FSS), interval-valued fuzzy soft sets (R-ITFSS), and intuitionistic fuzzy soft sets (R-ICFSS) for decision making. Therefore, this is the ﬁrst attempt on reduction approach of interval-valued intuitionistic fuzzy soft datasets. We also introduce an adjustable reduction approach of weighted intervalvalued intuitionistic fuzzy soft sets (AR-WIIFSS) and investigate its application for decision making. We make extensive analysis for AR-IIFSS and AR-WIIFSS approaches to show their feasibility in practical applications of decision making.
Digital Object Identifier (DOI)
Qin, Hongwu; ShukriMohd Noor, Ahmad; Ma, Xiuqin; Chiroma, Haruna; and Herawan, Tutut
"An Adjustable Reduction Approach of Interval-valued Intuitionistic Fuzzy Soft Sets for Decision Making,"
Applied Mathematics & Information Sciences: Vol. 11
, Article 7.
Available at: https://dc.naturalspublishing.com/amis/vol11/iss4/7