An Asymmetric Measure of Comparison of Neutrosophic Sets

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.