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Dynamic and Updating Multigranulation Decision-Theoretic Rough Approximations When Adding or Deleting Objects
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DOI：10.1109/ACCESS.2021.3110879.png)
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摘要
En 中文
Along with the development of big data, knowledge updating occurs in various situations, some scholars had studied the dynamic method of updating knowledge in multigranulation decision-theoretic rough sets when adding or deleting granular structures. However, there is no study about the case of adding or deleting objects, which limits the development of multigranulation decision-theoretic rough sets. Based on the matrix method, the dynamic knowledge updating of optimistic multigranulation decision-theoretic rough set and pessimistic multigranulation decision-theoretic rough set were studied in this paper, then given the static algorithm and dynamic algorithms, and the time complexity of three algorithms was analyzed. The theory and experimental results show that two dynamic algorithms are both more effective than the static algorithm.
Keyword:
Rough sets
Heuristic algorithms
Information systems
Approximation algorithms
Uncertainty
Licenses
Knowledge discovery
Incremental updating
knowledge discovery
multigranulation decision-theoretic rough sets
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
A Group Incremental Approach to Feature Selection Applying Rough Set Technique应用粗糙集技术的组增量式特征选择方法
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