A dominance-based multigranulation rough sets approach for dynamic updating approximations

With the variation of the collected data, useful information obtained dynamically from the information system plays an important role in decision making. The properties of updating approximations in dominance-based optimistic and pessimistic multigranulation rough sets were discussed. An approach to dynamically updating approximations in dominance-based optimistic and pessimistic multigranulation rough sets while adding a granulation structure in multigranulation environment was presented. The approach does not need to recalculate the dominance classes and approximations of each granulation structure in the universe. The dominance classes of each object were calculated with respect to the added granulation structure, and then the approximations can be obtained by the properties of updating approximations in dominance-based optimistic and pessimistic multigranulation rough sets which can improve the efficiency of updating approximations. The experimental results demonstrate the validity of the proposed approach while comparing with the static algorithm.