Roughness-Dice Similarity Measure Generalization for Rough Neutrosophic Set Application in Investment Company Selection

The results for similarity measure between two or more information given by the expert is classified as either a strong relationship or less important relationship. Therefore, it is important to get the results for similarity measure as the best conclusion for the information relationship. In this research, the roughness-Dice similarity measure was chosen as the similarity measure method based on the motivation of Dice similarity measure for rough neutrosophic set. Additionally, a rough neutrosophic set was chosen as the uncertainty set theory which encompasses the upper and lower approximation. The definition of the roughness-Dice similarity measure is generalized by roughness approximation. Subsequently, the derivation procedure used for investment company selection involving the roughness-Dice similarity measure of a rough neutrosophic set is presented. The similarity results were compared between the mean value and roughness approximation for a rough neutrosophic set. In conclusion, if either value of the similarity measure is nearest to one, hence a strong relationship is defined between the information given or vice versa.