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Dynamic updating approximations in multigranulation rough sets while refining or coarsening attribute values
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DOI：10.1016/j.knosys.2017.05.015.png)
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Abstract
En 中文
Multigranulation rough sets have attracted more and more attentions in recent years. In real-life applications, with the development of information technology, the attribute values often dynamically evolve over time. How to update useful knowledge is of great importance for dynamic information systems. Approximations of a concept are fundamental concepts of multigranulation rough sets, which need to be updated incrementally while refining or coarsening attribute values. Motivated by the requirements of dynamic knowledge acquisition due to refining or coarsening attribute values, in this paper, we present the dynamic mechanisms for updating approximations in multigranulation rough sets while refining or coarsening attribute values. Then, the corresponding dynamic algorithms for updating multigranulation approximations are designed on the basis of the proposed mechanisms. Extensive experiments on six data sets from UCI demonstrate that the proposed dynamic algorithms for updating approximations in multigranulation rough sets are more effective in comparison with the static algorithm. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Incremental learning
Knowledge acquisition
Multigranulation rough sets
Decision making
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