Intelligent Decision Making using a Chi-squared based Similarity Measure for Neutrosophic Soft Set

Résumé

Data, knowledge, and even queries in the real world are not necessarily accurate. The majority of them are imprecise, ambiguous, or both. In many situations, the data may be inconsistent also. Mathematical frameworks that cope with such imperfect or inconsistent data include neutrosophic set and neutrosophic soft set. Fabrication of database models that can handle imprecise information more precisely than existing models is ongoing in literature.  The notion of similarity measure plays a significant role in comparing alternatives for imprecise data or queries and is often used in decision-making for uncertain queries. This paper introduces a novel similarity measure on neutrosophic soft set based on the Chi-Squared metric. The applicability of the predicted measure in decision making is exhibited with suitable real-life examples in diversified domains. It is also demonstrated that the proposed measure can identify differences in crucial situations when existing measures fail and thus can help in intelligent decision making.