An Asymmetric Measure of Comparison of Neutrosophic Sets

Abstract

The single-valued neutrosophic (SVNS) set is a beneficial and significant tool to deal with
uncertainty with the neutrality of truth. This article introduces a non-conventional asymmetric
measure of comparison in the single-valued neutrosophic framework. Such a measure is applied to
the problems where the conventional symmetric measures of comparison do not produce valid
computational results and require a directed closeness or discrimination between two abstractions
represented by neutrosophic sets. We prove some properties of the proposed neutrosophic
comparison measure and empirically justify its utilization in a problem of strategic decision-making,
pattern recognition and medical diagnosis. The assessment of the performance of the proposed
measure using “Degree of Confidence†shows the advantage of the proposed measure.