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

• Published in: Springer Proceedings in Mathematics and Statistics
• Main Author: Alias S.; Mustapha N.; Yasin R.M.; Yusoff N.S.M.; Jusoh N.S.M.; Rahim N.S.H.; Homdan S.A.S.
• Format: Conference paper
• Language: English
• Published: Springer 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209558527&doi=10.1007%2f978-981-97-3450-4_1&partnerID=40&md5=ce01df7e658f9f3ee4add8fa4a9fe3ad

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. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.