Accurately quantifying the relationships between conditional and decision attributes is crucial for information systems to make well-informed decisions. This work presents a novel approach to verifying class membership using Similarity Rough Set Theory (SRS) and a unique symmetry-based point of view. We apply SRS to model attribute interactions and their impact on overall class membership in detail. For determining the membership degree, two methods are provided: one that depends on individual attribute and the second method takes all attributes into consideration.  The efficiency of the proposed work with respect to the existing one is clarified by presenting a case study of higher education university information system. The results illustrates that the membership degrees obtained by individual and aggregated attribute are effective methods, indicating a good relations of class membership. In addition, it shows the practical effectiveness of the proposed technique by applying it on a large dataset of academic student marks. This work contributes in evaluating and verifying the class membership for more informed decision in the real-life applications.