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In this paper, we have presented a novel approach to efficiently update approximations of dominance based rough set theory when data evolve at time t+1, new objects enter the system and old objects leave the system. Two algorithms have been proposed, update approximations when new objects enter the system at time t+1 and update approximations when old objects leave the system at time t+1. To evaluate the performance of proposed approach, experiments performed on UCI publicly available datasets with variation ratio of 5% of original dataset size.
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IEEE/ICACT20220156 Slide.16
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